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Classification of 3D maxillary incisor root shape

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Classification of 3D maxillary incisor root shape

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Classification of 3D Maxillary
Incisor Root Shape
By
Courtney Clayton
_____________________________________________________________________________________
A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
CRANIOFACIAL BIOLOGY

May 2018

Copyright 2018 Courtney Clayton
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Acknowledgements

This project would not have been possible without my mentors, Dr. Glenn Sameshima and Dr. Andre
Weissheimer. Thank you for your guidance and support.
I would also like to thank the Herman Ostrow School of Dentistry Orthodontic Class of 2020 and Dr.
Glenn Sameshima, Dr. Dan Grauer, Dr. Ivan Shnorhokian, Dr. Harry Dougherty Jr., and Dr. Andre
Weissheimer for participating in the classification reliability testing of this project. In addition, I
appreciate Dr. Kaifeng Yin for his technical assistance.
Thank you to my family for their unwavering support throughout my education. I would not know what I
would do without your love and encouragement.

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Table of Contents
Acknowledgements……………………………………………………………...2
List of Figures…………………………………………………………………...4
List of Tables…………………………………………………………………....5
Abstract……………………………………………………………………….....6
Chapter 1: Introduction……………………………………………………….....7
Chapter 2: Review of Literature……………………………….………………..13
Root Resorption: Definition and Mechanism.…………………...……...13
Role of Root Morphology..……………………………………..………16
Inconsistencies in Root Shape Classification...…………....……………18
Diagnostics and Imaging………...……………………………………...23
Classification Design...………………………………………………….27
Intra-Rater and Inter-Rater Reliability..………………………………...30
Clinical Significance……………………………..…....………………..32
Chapter 3: Hypotheses………………………………………………………….34
Research Hypotheses …………………………………………………..34
Null Hypotheses……………………………....………...…...………….34
Chapter 4: Materials and Methods……………………………………………...35
Validation of Methodology
Chapter 5: Results………………………………………………………………50
Chapter 6: Discussion…………………………………………………………..56
Chapter 7: Conclusions…………………………………………………………62
References..……………………………………………………………………..63
Appendix...…………………..………………………………………………….72

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List of Figures

Figure 1: Levander and Malmgren (1988) root shape classification guide

Figure 2: Mirabella and Artun (1995) root shape reference guide

Figure 3: Mirabella and Artun (1995) adaptation in Witcher article (2008)

Figure 4: CBCT segmentation of maxillary left central incisor using ITK-SNAP software

Figure 5: CBCT segmentation of maxillary right central incisor using ITK-SNAP software

Figure 6: CBCT segmentation of maxillary left lateral incisor using ITK-SNAP software

Figure 7: 3D model of maxillary left lateral incisor (labial aspect) using VAM viewing software

Figure 8: 3D model of maxillary left central incisor (labial aspect) using VAM viewing software

Figure 9: 3D model of maxillary right lateral incisor (mesial aspect) using VAM viewing
software

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List of Tables

Table 1: Maxillary Central Incisor Root Shape Classification Guide

Table 2: Maxillary Lateral Incisor Root Shape Classification Guide

Table 3: Maxillary Incisor Root Length and Shape Assessment from Experienced Orthodontists

Table 4: Maxillary Incisor Root Length and Shape Assessment from Orthodontic Residents

Table 5: Frequency Data for Root Shape and Length Assessment in Experienced Orthodontists

Table 6: Frequency Data for Root Shape and Length Assessment in Orthodontic Residents

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Abstract
Background: Orthodontically induced root resorption has been a growing concern in the field of
orthodontics. There is a higher prevalence of root resorption in the maxillary anterior teeth,
especially the maxillary lateral incisor. The root of the maxillary lateral is most commonly
abnormally shaped. Several studies in the literature have contradictory reports for the different
root shapes of maxillary incisors and the susceptibility of root resorption. There are several
different subgroups for classifying roots across studies. Most of the abnormal root shape studies
use 2-dimensional images. Three dimensional CBCT imaging provides a better diagnostic tool
for evaluating root shape and root resorption.
Purpose: The purpose of this study was to determine the following: 1. The prevalence of
abnormal root morphology in orthodontic subjects. 2. If a comprehensive subjective
classification scheme for maxillary incisor root forms can be constructed from 3D surface
models using basic geometric shapes and common arch forms used in architecture for criteria
descriptors. 3. The reliability of a subjective 3D classification scheme for diagnosis of abnormal
root forms. 4. The clinical significance of a subjective classification method for common use in
the office.
Materials and Methods: This study was conducted at the Herman Ostrow School of Dentistry of
USC Department of Orthodontics. A database of pre-treatment cone-beam computed
tomography (CBCT) images was examined alphabetically. Cases were selected for the study
based on a set of selection criteria. ITK-SNAP software was used to create 3D models of the
maxillary centrals and laterals of each patient. All incisors were subjectively categorized based
on geometric shapes and common arch forms from the study of architecture. Provided with the
classification qualifications for each category, resident orthodontists and practicing orthodontists
evaluated the 3D models and subjectively categorized each of them.
Results: Of the 60 records reviewed, 13 patients were found with the inclusion criteria (4 males
and 9 females, sex ratio 1:2.25) with an age range of 16 to 39 years. The prevalence of
abnormally shaped roots in this sample was 87.5% from the experienced orthodontists and
88.4% from orthodontic residents. Experienced orthodontists identified 81.7% of the incisors as
being normal in length and the orthodontic residents identified 50% of the incisors as normal in
length. Assessor intraclass agreement on length ranged from .453-.688 for experienced
orthodontists and .251-.473 for orthodontic residents. Assessor intraclass agreement on shape
ranges from .166-.625 for the experienced orthodontist and from .308-.452 for the orthodontic
residents.
Conclusion: There is a high prevalence of identifiable abnormally shaped maxillary incisor roots.
The classification scheme for maxillary incisor root forms was constructed using basic geometric
shapes and arch forms and enabled assessors to categorize roots. However, there was a low to
moderate inter- and intra-class agreement for classifying incisors based on root length and shape.
Therefore, the subjective incisor root classification criteria as presented in this study is not
reliable and needs to be revised in order for each tooth to fall into a single category. More
importantly, the method for testing the reliability of the categories needs to be more systematic
and regulated for each assessor in each round.

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Chapter 1: Introduction
Orthodontic induced root resorption (OIRR) remains a great concern in the orthodontic
field. Excessive orthodontic force, exceeding the optimum force of 7-26 grams per centimeter,
causes a shortening of the root. Sameshima and Sinclair (2001) concluded an average resorption
of around 1.2–1.5 mm per incisor occurs due to root movement, which is approximately a 10%
shortening of the root.
Most studies report that maxillary teeth are more susceptible to resorption than
mandibular teeth (Brezniak, 1993). Maxillary anterior teeth, particularly the lateral incisors
(Sameshima and Sinclair, 2001), are the most severely affected by orthodontically induced root
resorption. The second most affected tooth is the maxillary central incisor, followed by the
maxillary canines and mandibular canines (Sameshima and Sinclair, 2001). Horizontal
movement of the root apex is associated with OIRR in central incisors and apical root width was
negatively associated with OIRR in lateral incisors (Mirabella and Artun, 1995).
Several factors contribute to the root’s susceptibility to root resorption. Genetic
predisposition, individual biologic variability and mechanical factors influence root resorption
(Bartley et al., 2011; Weltman et al., 2010; Zahrowski & Jeske, 2011). Genetic factors account
for at least 50% of the variation in external apical root resorption. Potential risk factors for OIRR
include: previous root resorption (Brezniak & Wasserstein, 1993; Hartsfield, 2004; Marques et
al., 2010), tooth/root morphology, length and roots with developmental abnormalities (Brin,
2003; Fox, 2005; Marques et al., 2010; Sameshima & Sinclair, 2001, 2004; Smale et al., 2005),
systemic factors (Adachi, 1994; Igarashi et al., 1996), genetic influences (Bollen, 2002;
Hartsfield et al., 2004; Sameshima & Sinclair, 2001), previous trauma (Brezniak & Wasserstein,
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2002; Brin, 2003; Hartsfield, 2004), severity and type of malocclusion (Brin, 2003; Sameshima
& Sinclair, 2001; Segal et al., 2004).
Mirabella and Artun (1995) found that if orthodontic force is concentrated at a particular
region of a deviated root shape, root resorption will occur. Many studies on risk factors for root
resorption have confirmed that pre-treatment root length and root shape are associated with
increased root resorption during orthodontic tooth movement. Teeth with abnormal apices are at
greater risk than those with normal apices (Sameshima and Sinclair, 2004). Maxillary laterals
have the greatest incidence of abnormal root shape (Sameshima and Sinclair, 2001). The
maxillary central incisor has the highest incidence of pointed roots and either the maxillary
central or maxillary lateral have the highest incidence of dilaceration (Samesima and Asgarifar,
2001).
Several authors have done studies and have concluded which root shapes undergo the
most resorption. However, there is disagreement in the results among these studies, especially for
the risk of blunt and short roots. Bent or dilacerated roots, pipette-shaped roots, long pointed
roots, and previously resorbed roots have been shown to be at risk (Brezniak, 1993; Kjaer, 1995;
Kurol, 2000; McFadden, 1989; Mirabella and Artun, 1995; Sameshima and Sinclair, 2001;
Smale, 2005). Sameshima and Sinclair (2001) reported that patients with long or pointed roots
before treatment underwent significant root shortening during orthodontic treatment. The pipette-
shaped root was shown to be the most susceptible root form to root resorption. Dilacerated teeth
are also among the highest to undergo resorption. The risk for blunted or short roots have been
seen as more susceptible and as less susceptible, depending on the study. According to a study
done by Kamble (2012), a decrease in crown to root ratio is thought to increase the loading on
the root, enhancing root resorption. Most authors (Harris, 1997; Kjaer, 1995; Goldson and
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Henrickson, 1975; McFadden, 1989; Newman, 1975; Taithongchai et al (1996); Thongudomporn
and Freer, 1998) agree that short roots undergo enhanced root resorption. However, Mirabella
and Artun (1995), Levander and Malmgren (1988) and Goldin (1989) found opposite findings.
A study by Oyama (2007) was done to understand the difference of stress distribution at
the root apex according to the deviated root shape. Therefore, showing the importance of
identifying the abnormal root shape early to prevent damage to the root. Using the root shape
classification derived from Levander and Malmgren (1988), they tested stress results in five
different root shapes: normal, short, blunt, distally bent, and pipette-shaped. The short root model
showed significant stress at the middle of the root. The blunt-shaped root model showed no
significant stress concentration. In the bent-shaped model, intrusive force caused stress
concentration at the mesial and distal surface of the root apex and lingual force led to stress at the
labial and lingual surface of the root apex. Lastly, the pipette-shaped root model demonstrated a
concentrated stress at the neck of the root apex, regardless of the force direction applied.
The inconsistencies in the data regarding root shape may be due to the various terms and
criteria defining root shape classifications and the means of diagnosing the root shapes across
studies. Marques (2010) describes what some authors call pipette-shaped roots as triangular
roots. Some authors call normal appearing roots as triangular. Kjaer et al described a tooth as
being short if the root was of equal length as the crown. Newman et al (1975) described a tooth
was “blunted” if tooth loss approximated 2mm and “moderately shortened” if more than 2mm
but less than one-third of the root was gone. Root dilacerations are defined as the abrupt
deviation of the long axis of the crown or root portion of the tooth, which is due to a traumatic
nonaxial displacement of already formed hard tissue in relation to the developing soft tissue
(Topouzelis, 2010). Some authors (Hamasha, 2002; Malcic, 2006) characterize a dilacerated root
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as having a 90 degree angle or greater along the axis of the tooth or root in a mesial or distal
direction. Others describe a root as dilacerated if there is at least a 20 degree apical deviation
from the normal axis of the tooth (Chohayeb, 1983). The bent shape of these teeth induces root
resorption and should be taken into consideration prior to orthodontic treatment, which is why
the proper diagnostic records are important.
Orthodontic diagnostic records should be comprehensive for diagnosis and treatment
planning. The radiographic records most often include a panorex and lateral cephalogram.
However, panoramic films showed a poor ability to detect abnormal root form (Witcher, 2010).
They also cannot be used to properly diagnose root dilacerations, which can occur in the lingual,
buccal, mesial, and distal direction. Root dilacerations and other abnormal shapes, visible on
periapical films, often appeared normal on panoramic films. Therefore, periapical views at
different angles or an upper standard occlusal are recommended to accompany the panorex when
there is a clinical implication (Fergusson, 1992). Periapical radiographs are necessary to more
precisely evaluate the root morphology. However, though mesial and distal dilacerations appear
somewhat clear, labial and lingual dilacerations appear as a “bull’s eye” (Nabavizadeh, 2013).
Previous studies using cone-beam computed tomography (CBCT) have demonstrated high
accuracy and reliability of measurements of root length when CBCT images are compared both
to direct skull measurements and to periapical radiographs (Ahlbrecht and Ruellas, 2017). Cone-
beam computed tomography (CBCT) allow clinicians to assess the exact positions of the apex
and the crown, and the degrees of root formation and dilaceration (Walia, 2016). 3D shape
analysis using CBCT images allows phenotypic characterization and can be used to evaluate root
size and shape pre- and post-treatment (Ahlbrecht and Ruellas, 2017).
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Most of the studies about incisor root morphology and root resorption utilized 2-
dimensional radiographic images. Levander and Malmgren (1988) classified teeth as either
normal, blunt, short, dilacerated, or pipette-shaped, which is the reference for several studies
regarding root shape (Kamble, 2012; Nigul, 2006; Oyama,2007). Many studies (Smale, 2005;
Witcher, 2008 ) that included subjective scoring of root form also use an adaptation of the
reference guide developed by Mirabella and Artun in 1995. In this study, mesial distal root width
was measured 4mm incisal to the root apex. Root form was scored subjectively as normal,
pointed, eroded, blunt, bent, and bottle shaped.
Ahlbrecht and Ruellas (2017) applied a 3D surface mapping technique and quantitatively
defined subgroups for incisor root morphology. They proposed a 3Dimensional characterization
for both maxillary central and lateral incisor individually, using an adaptation of classification
descriptions from previous 2D classification studies (Levander and Malmgren, 1988; Mirabella
and Artun, 1995). Each subgroup shared phenotypic characterization complying with the
proposed criteria, excluding pipette-shaped or bottle-shaped, eroded, and pointed. The study
showed statistically significant root morphological differences when the blunt, long, conical, and
short roots were compared to the neutral. Sameshima and Sinclair (2004) classified teeth into six
categories: normal, blunted, pipette or bottle-shaped, pointed, dilacerated, and incomplete
(incomplete apex). Thongudomporn and Freer (1998) classified roots using even fewer
categories. Consolaro (2005) also proposed a classification: triangular, rhomboid, pipette, and
dilacerated.
The 2D study performed by Mirabella and Årtun (1995) showed patients with bottle-
shaped or eroded root shape. However, the 3D study performed by Ruellas (2017) of adolescent
patients did not report any bottle-shaped roots. It was not clear if the bottle-shaped or eroded
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morphology was due to aging or the result of the limitations of a 2D analysis versus a 3D one.
This study evaluates root morphology of patients from ages 16 to 35, which allowed analysis of
root differences with age.
Knowing the different root sizes and shapes, along with the susceptibility of each to root
resorption, is helpful for diagnostic purposes and for patient education. The classification
subgroup criteria needs to be outlined and described clear enough for clinicians to make a
subjective decision in a timely manner. From childhood, shapes are used to categorize what one
sees, allowing us to define and organize the world around us (Church, 2018). To date, there is no
incisor root shape classification system that uses shape-based object recognition with geometric
shapes and common arch forms used in architecture.
As orthodontists, diagnosis and treatment planning are crucial components of orthodontic
treatment. Though a thorough analysis of a segmented 3D model of each tooth would be helpful,
it just is not practical. During the initial consultation, quantitative analysis of each root is time-
consuming and unrealistic. Most orthodontists that do take a CBCT, evaluate the 3D rendered
image in an imaging software, such as Dolphin Imaging. This pilot study involves constructing
classification subgroups for maxillary incisor root shape, using common geometric shapes and
architectural arch forms. The reliability of the subjective classification subgroups is then tested,
to refine the categories.

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Chapter 2: Review of Literature
Root Resorption: Definition and Mechanism
Orthodontically induced inflammatory root resorption (OIRR) is a pathological
consequence of orthodontic force (Brezniak et al, 2002). External apical root resorption develops
as the natural protection of the precementum and cementoblasts are damaged or removed (Leach
2001), or when resorption exceeds the reparative capacity of cementum (Hartsfield, 2009). One
theory is that excessive force and hyalinization of the periodontal ligament result in hyperactivity
of cementoclasts and osteoclasts (Travess,2004). Microscopic signs of external resorption are
very common with or without orthodontic treatment (Henry, 1951) however, macroscopic
evidence of resorption has been reported in up to 40% of adults and 16.5% of children following
orthodontic movement (Mirabella and Artun, 1995). Resorption associated with orthodontic
forces is most commonly observed in the apical region, resulting in shorter roots. Sameshima and
Sinclair (2004) report a shortening of 10%, averaging around 1.2-1.5mm per incisor. Usually
once the forces are removed, repair with new cementum and periodontal fibers occurs with
recontouring of the root (Leach 2001).
Schwartz (1932) advocated for the optimal force level for tooth movement to be between
7 and 26 grams per square centimeter. When the force exceeds this amount, root resorption
occurs. King and Fischlschweiger (1982) found that light forces produced insignificant root
resorption. Likewise, Linge and Linge (1983) concluded that removable appliances cause
significantly less apical root resorption than do fixed appliances. The response to orthodontic
force, however, depends on an individual’s genetic background (Hartsfield, 2009). Genetic
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factors account for at least 50% of the variation in external apical root resorption, with variation
in the interleukin-1-beta gene accounting for 15% (Hartsfield, 2004).
Most studies report that maxillary teeth are more susceptible to resorption than
mandibular teeth (Brezniak, 1993). Maxillary anterior teeth, particularly the lateral incisors
(Sameshima and Sinclair, 2001), are the most severely affected by orthodontically induced root
resorption. The sample mean resorption of each pair of maxillary centrals and laterals is less than
1.5mm (Linge and Linge 1991, Linge and Linge1983, Mirabella and Artun 1995b, Sameshima
and Sinclair, 2001). About 4% of patients experience generalized resorption of more than 3mm
(Mirabella and Artun 1995b) and 25% of patients experience resorption greater than 2mm
(Sameshima and Sinclair, 2001). During orthodontic treatment, about 3% of adults (Mirabella
and Artun 1995b) and 2% of adolescents (Linge and Linge, 1983) are likely to have at least one
tooth that resorbs more than 5mm. Smale et al reported 15.5% of patients have at least one
maxillary incisor with at least 2mm of resorption from 3 to 9 months after the initiation of fixed
appliance therapy.
Several authors have reported that the etiology of root resorption is complex and
multifactorial. Genetic predisposition, individual biologic variability and mechanical factors
influence root resorption (Bartley et al., 2011; Weltman et al., 2010; Zahrowski & Jeske, 2011).
Potential risk factors for OIRR include: previous root resorption (Brezniak & Wasserstein,
1993; Hartsfield et al., 2004; Marques et al., 2010), tooth/root morphology, length and roots with
developmental abnormalities (Brin et al., 2003; Fox, 2005; Marques et al., 2010; Sameshima & Sinclair,
2001, 2004; Smale et al., 2005), systemic factors (Adachi et al., 1994; Igarashi et al., 1996), genetic
influences (Bollen, 2002; Hartsfield et al., 2004; Sameshima & Sinclair, 2001), previous trauma
(Brezniak & Wasserstein, 2002; Brin et al., 2003; Hartsfield et al., 2004), severity and type of
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malocclusion (Brin et al., 2003; Sameshima & Sinclair, 2001; Segal et al., 2004). There is no definitive
conclusion as to whether sex (Harris et al., 1997; Hendrix et al., 1994; Sameshina & Sinclair, 2001), age
(Baumrind et al., 1996; Costopoulos & Nanda, 1996; Harris et al., 1997; Harris & Baker, 1990; Owmann-
Moll et al., 1995), and duration of active treatment (Baumrind et al., 1996; Beck & Harris, 1994; Harris et
al., 1997; Kaley & Phillips, 1991; Kurol et al., 1996; Mirabella & Artun, 1995; Sameshina & Sinclair,
2001) are risk factors for root resorption.
Very few studies have been done on the association between ethnicity and root resorption.
Sameshima and Sinclair (2001) found that Asian patients experience less root resorption than white or
Hispanic patients. Though, there was an insufficient number of African Americans for a valid statistical
comparison. Increased overjet was also associated with greater resorption
Mirabella and Artun (1995) claim that endodontic treatment is a preventive factor for lateral
incisors and canines. While others state endodontic treatment as a risk factor (Brezniak & Wasserstein,
2002; Hamilton et al., 1999). As far as treatment mechanics, the use of elastic forces may increase the risk
of resorption on the teeth supporting the elastics. Horizontal movement of the root apex is associated with
OIRR in central incisors and apical root width was negatively associated with OIRR in lateral incisors
(Mirabella and Artun, 1995). In terms of root location, Kaley and Phillips concluded that apical root
resorption was 20 times greater when the maxillary incisors were in close proximity to the lingual or
cortical plate.
Maxillary laterals are the most commonly resorbed teeth in the dentition, followed by the
maxillary centrals, maxillary canines, and mandibular canines (Sameshima and Sinclair, 2001).
Maxillary laterals are also the most commonly dilacerated teeth, while the maxillary centrals
have the highest incidence of being pointed. Maxillary laterals have the greatest incidence of
abnormal root shapes (Sameshima and Sinclair, 2001).
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Linge and Linge (1983) found that patients starting orthodontic treatment after age 11
experienced significantly more root resorption than patients starting earlier. The amount of root
resorption increases with increasing root length (Mirabella and Artun 1995a, Sameshima and
Sinclair 2001b) and root width (Mirabella and Artun 1995a, Sameshima and Sinclair 2001b, and
Taithonchai 1996). A study done by Smale et al showed that wide roots and normal root form are
actually preventive factors for RR in maxillary incisors. He also confirmed narrow, pointed, and
deviated roots to be risk factors.
Role of Root Morphology
Hertwigs epithelial root sheath (HERS) determines the number, length, and shape of the
root, induces the formation of radicular dentin, and is involved in root cementum development
(Luder, 2015). Impairment or disruption of HERS processes can affect the development of the
root (Luder, 2015), such as in the case of shortened roots. Direct trauma to a developing tooth is
frequently the cause of premature arrest of root development (Andreasen and Flores, 2007).
Andreasen (1985) and Tronstad (1988) claim that short or misshapen roots are usually a
consequence of hard tissue resorption, secondary to dento-periodontal traumas, local periodontal
inflammation, or orthodontic tooth movement using excessive forces.
If orthodontic force is concentrated at a particular area of the apical root, root resorption
may arise. Sameshima and Sinclair et al (2004) reported teeth with abnormal root morphology
displaying external root resorption more frequently than those with normal root shape. Mirabella
and Artun e al (1995) also state abnormal root shape as a significant risk factor for root
resorption. Even after 6-9 months of orthodontic treatment, an irregular shaped root indicates a
risk for severe root resorption (Levander and Malmgren, 1988). Bent or dilacerated roots,
pipette-shaped roots, long pointed roots, and previously resorbed roots have been shown to be at
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risk (Brezniak, 1993; Kjaer, 1995; Kurol, 2000; McFadden, 1989; Mirabella and Artun, 1995;
Sameshima and Sinclair, 2001; Smale, 2005).
There are some inconsistencies between studies regarding which incisor root shape
influences the risk for root resorption. Most authors (Harris, 1997; Kjaer, 1995; Goldson and
Henrickson, 1975; McFadden, 1989; Newman, 1975; Taithongchai et al (1996); Thongudomporn
and Freer, 1998) agree that short roots undergo enhanced root resorption. Nigul (2006) reported
that short roots resorbed almost twice as much as any other root form. However, Mirabella and
Artun (1995), Levander and Malmgren (1988) and Goldin (1989) found opposite findings.
A blunt shape increases the degree of root resorption significantly compared to the
normal shaped root, terming it as a moderate risk factor (Levander and Malmgren, 1988).
Thongudomporn and Freer (1988) and Brezniak (2002) also claim blunt roots are more
susceptible, while Sameshima and Sinclair (2001) report a decrease in risk. Smale et al (2005)
also found no indication of short, blunt roots to be at a greater risk.
In most studies, a bend in the root increased the risk for root resorption (Levander and
Malmgren, 1988; Mirabella and Artun, 1995; Newton, 1975; Sameshima and Sinclair, 2001). A
pipette-shape also enhances root resorption (Newton, 1975; Sameshima and Sinclair, 2001;
Thongudomporn and Freer, 1988) and is considered a high risk factor (Levander and Malmgren,
1988). Likewise, a bottle-neck or bottle-shaped root increases its susceptibility to resorption
(McFadden et al, 1989; Sameshima and Sinclair, 2001).
A study by Oyama (2007) was done to understand the difference of stress distribution at
the root apex according to the deviated root shape. Using the root shape classification derived
from Levander and Malmgren (1988), they tested stress results in five different root shapes:
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normal, short, blunt, distally bent, and pipette-shaped. The short root model showed significant
stress at the middle of the root. The decrease in root to crown ratio enhances the loading on the
root. The blunt-shaped root model showed no significant stress concentration, which contradicts
the previous findings of Levander and Malmgren (1988) and Thongudomporn and Freer (1998)
that observed resorption more frequently in blunt-shaped roots compared to normal shaped roots.
In the bent-shaped model, intrusive force caused stress concentration at the mesial and distal
surface of the root apex and lingual force led to stress at the labial and lingual surface of the root
apex. Lastly, the pipette-shaped root model demonstrated a concentrated stress at the neck of the
root apex, regardless of the force direction applied.
Kamble (2012) performed a study to clarify the influence of orthodontic forces (intrusion,
extrusion, tipping, and rotational) on various root forms (classified by Levander and Malmgren,
1988). They reported that extrusive and intrusive forces increases the biomechanical burden on
the blunt root apex. The dilacerated model showed stress at the middle and apical regions of the
root during all four (intrusion, extrusion, tipping, and rotational) force applications. The results
for the short and pipette models were similar to Oyama’s findings (2007). Kamble (2012)
concluded that the dilacerated root was the most affected root morphology, followed by the
pipette-shaped root form.
Inconsistencies in Root Shape Classification
Wheeler describes the average shape of each tooth in the dentition. He describes the root
of the central incisor from the labial aspect as cone-shaped, with a relatively blunt apex in most
instances, and a root 2 or 3mm longer than the crown length. The maxillary lateral incisor varies
in form more than any other tooth in the mouth, except the third molar. The root of the lateral is
usually as long, or longer than that of the central incisor. It is often about 1.5 times the length of
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its crown, and tapers evenly from the cervical line to a point approximately two thirds of its
length apically. The root of the lateral typically curves sharply from that point in a distal
direction, ending in a pointed apex. However, some roots are straight and others even curve
mesially. Chohayeb (1983) reports 52% of the maxillary laterals evaluated have a distolabial
deviation while only a 2.1% have straight roots, less than 20 degrees from normal axis. Though
Wheeler did not have 3D analysis for each tooth for which he provides an average, for years,
dental professionals describe his morphological descriptions as standard (Kamble, 2012; Oyama,
2007).
Many of the studies on root apparatus focus on the pulp chamber morphology for
endodontic reasons. When measuring root canal curvature for endodontic purposes, Abesi and
Ehsani (2011) used the Seidberg classification: the degree of the curvature was categorized as
low (<5°), moderate (5-25°) and severe (25-70°). They observed that 62% of maxillary anterior
teeth had root curvatures. In 2012, Fantozzi performed a study to statistically analyze the root
anatomy of European anterior teeth using a manual calliper. The average radicular length,
radicular diameters and root tapering, and radicular apex angle were calculated from a sample of
extracted anterior teeth. A statistical elaboration of the data was performed to highlight the shape
variations of different sides of the root surfaces, describing 12 parameters for each single-root
tooth.
Maxillary incisor root morphology has been characterized in some orthodontic studies,
using 2-dimensional radiographic images. The variety of different classifications used may be a
factor in the inconsistencies of the data involving root shape. Levander and Malmgren (1988)
classified teeth as either normal, blunt, short, dilacerated, or pipette-shaped, which is the
reference for several studies regarding root shape (Kamble, 2012; Nigul, 2006; Oyama,2007).
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Figure 1. Levander and Malmgren (1988) root shape classification guide. 1. Short 2. Blunt 3. Bent 4.
Pipette

Many studies (Smale, 2005; Witcher, 2008 ) that included subjective scoring of root form
also use an adaptation of the reference guide developed by Mirabella and Artun in 1995 (Fig.2).
In this study, mesial distal root width was measured 4mm incisal to the root apex. Root form was
scored subjectively as normal, pointed, eroded, blunt, bent, and bottle shaped.

Fig. 2. Mirabella and Artun (1995) root shape reference guide. Normal (N), blunt (A), eroded (B), pointed
(C), bend (D), bottle shaped (E).

Fig. 3. Mirabella and Artun (1995) adaptation in Witcher article (2008)

Sameshima and Sinclair (2004) classified teeth into six categories: normal, blunted,
pipette or bottle-shaped, pointed, dilacerated, and incomplete (incomplete apex).
Thongudomporn and Freer (1998) classified roots using even fewer categories. Consolaro (2005)
also proposed a classification: triangular, rhomboid, pipette, and dilacerated.
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There are various definitions and criteria of each incisor root shape across relevant
studies, which result in contradictions and inconsistencies within collected data on the
prevalence and risks involved with each root form. The definition of dilaceration varies greatly
in the literature. Tomes (1848) first defined dilaceration as a deviation or bend in the linear
relationship of a crown of a tooth to its root. Andreason (1971) defines a root dilaceration as a
sharp bend of either the crown or root axis. It is not to be confused with a flexion, which is a
smooth physiological or abnormal curvature of the root (Jafarzadeh and Abbott, 2007). Some
authors (Hamasha, 2002; Malcic, 2006) characterize a dilacerated root as having a 90 degree
angle or greater along the axis of the tooth or root in a mesial or distal direction. Others describe
a root as dilacerated if there is at least a 20 degree apical deviation from the normal axis of the
tooth (Chohayeb, 1983). With this definition, Chohayeb (1983) reported 98% of lateral incisors
are dilacerated. However, others (Silva et al., 2012) have found results near 3%.
It is important that clinicians differentiate between a short root or blunted root. Lind
(1972) was first to describe the short root anomaly (SRA) as full root formation with a
genetically determined foreshortening and should not be confused with a resorbed root.
Consolaro (2004) describes apices as rounded or irregular after external apical root resorption
apices. Newman et al (1975) described a tooth was “blunted” if tooth loss approximated 2mm
and “moderately shortened” if more than 2mm but less than one-third of the root was gone.
Severely shortened teeth displayed a loss of more than one-third of the root. Kjaer et al described
a tooth as being short if the root was of equal length as the crown. Taithongchai (1996)
quantitatively evaluates root contour, subtracting a more apical root width from a more incisal
width. He indicates a blunt tooth with a value close to zero and a “pipette-shaped” root with a
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larger positive number. Marques (2010) describes what some authors call pipette-shaped roots as
triangular roots.
Ahlbrecht and Ruellas (2017) applied a 3D surface mapping technique and quantitatively
defined subgroups for incisor root morphology. They proposed a 3Dimensional characterization
for both maxillary central and lateral incisor individually, using an adaptation of classification
descriptions from previous 2D classification studies (Levander and Malmgren,1988; Mirabella
and Artun, 1995). Each subgroup shared phenotypic characterization complying with the
proposed criteria, excluding pipette-shaped or bottle-shaped, eroded, and pointed. Ahlbrecht and
Ruellas add short, long, and conical, along with mesial or distal directionality to the dilaceration
model. Subgroup averages were created using surface averaging of corresponding teeth and
visually compared to the neutral average model using semi-transparent overlays. The reference
group approximated the overall sample average and was called neutral. The study showed
statistically significant root morphological differences when the blunt, long, conical, and short
roots were compared to the neutral.
Neutral, blunt, long, conical, short, distal dilaceration, and mesial dilaceration root
morphologies were observed. Morphology subgroup criteria from Ahlbrecht and Ruellas (2017):
Blunt—root that presented apices notably shorter and less pointed, while the cervical
region was wider than the overall sample average
Long—root larger than the overall sample average in all dimensions (length and
circumference)
Conical—root narrower in circumference, but may or may not have increased length
compared to the overall sample average
Short—root that presented obvious decrease in apical length and narrower circumference
compared to the overall sample average
Dilacerated—root that presents distal, mesial or lingual dilaceration compared to the
overall sample average
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Bottle-shaped and eroded roots (Mirabella and Artun, 1995a) were not observed in the
study and only small dilacerations were seen. Erosions and bottle- or pipette-shaped root forms
may be the result of normal and abnormal root loading during aging. Variation in root form
observations may be due to the sample size, patient age, or observer interpretation. Another
significant source for different shape interpretations are 2D versus 3D visualization methods.
Diagnostics and Imaging
Dental panoramic tomographs are taken to determine the position of teeth and roots,
presence or absence of unerupted teeth of the normal series, supernumerary teeth and pathology
(Mattick, 1999). Dental patients are exposed to an effective dose equivalent to 0.007mSv with a
panorex, which may be raised by .008mSv with an additional intra-oral radiograph. The
combined exposure is estimated to be between a 1 in 3 million (Richardson, 1990) to 1 in 7
million (Danforth, 1980) risk of causing a radiation induced fatality.
The panoramic tomogragh showed a poor ability to detect abnormal root form (Witcher,
2010). The British Orthodontic Society Radiography Guidelines states that an upper standard
occlusal radiograph may be necessary to supplement the panoramic tomograph (Isaacson, 1994)
due to the narrow focal trough in the incisor region, sometimes causing the apices and palatal
structures to be out of focus. Roots outside the focal trough that are positioned lingually/palatally
to it, excessively proclined or retroclined, may appear magnified. Panoramic films also can not
be used to properly diagnose root dilacerations, which can occur in the lingual, buccal, mesial,
and distal direction. Therefore, periapical views at different angles or an upper standard occlusal
are recommended to accompany the panorex when there is a clinical implication (Fergusson,
1992).
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Sameshima and Asgarifar et al. report that using a panoramic radiograph to evaluate root
resorption may overestimate the root loss by 20%. The panoramic film does provide the
advantages of less patient chair time, less radiation exposure, less operator time, and
visualization of the entire lower face and joints. However, the magnification within the skull
varies at approximately 20-35% enlargement, with low reliability in the horizontal dimension.
Panoramic films tended to underestimate the amount of osseous destruction that appeared on
periapical film. Periapical film has a magnification factor of less than 5% and accurate to within
.3mm (Sameshima and Asgarifar, 2001).
The paralleling technique involves the film packet being placed parallel to the teeth and
the x-ray tubehead being at right angles to both the teeth and film packet, resulting in a
geometrically accurate result for assessing external resorption. The bisected angle technique is
not as reproducible as this paralleling technique, making it more difficult to evaluate root
resorption over time. Though reproducible, the paralleling technique is subject to procedural,
orientation, and projection errors which may result in elongation, foreshortening, and anatomical
structure overlapping (Sherrard, 2010). Angular changes between the tooth and the film
significantly affect periapical radiograph-based linear measurements, such as tooth length
(Brezniak, 2004; Gher, 1995; Ongkorahadio, 2000). Periapical views also do not allow a clear
diagnosis of a lingual or labial dilaceration. Though mesial and distal dilacerations appear
somewhat clear, labial and lingual dilacerations appear as a “bull’s eye” (Nabavizadeh, 2013).
The upper standard occlusal radiograph captures the anterior part of the maxilla and the
upper anterior teeth on film, allowing examination of root form and any external root resorption.
However, the upper standard occlusal is in effect a large bisected angle technique periapical and
is therefore not easily reproduced and subject to distortion. Great caution must be taken when
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using the bisecting technique to analyze the progression of root resorption. The lateral
cephalometric radiograph does provide an accurate and reproducible view of the length of the
upper incisors. Though, it is likely to be subjected to a 5-12% enlargement factor due to the
radiographic setup (Leach, 2001).
In the Witcher et al study done on the comparison of UAO’s and DPT’s when diagnosing
abnormal root shape, the DPT has a sensitivity of 45.6% and a specificity of 71.4% for detecting
abnormal root form. These findings were reported using UAO as a gold standard, however, the
sensitivity and specificity of UAO are also below 100%.
Tooth movement makes it more difficult to assess the exact amount of apical root loss,
especially if the tooth was tipped or torqued (Brezniak, 1993). Conventional radiographic
techniques are limited by superimposition and misrepresentation of structures, geometric
distortion, and magnification. Digital reconstruction has been shown to correct for different
projection angles and monitor the effect of tooth movement (Reukers, 1998). Cone-beam
computerized tomography (CBCT), a radiographic alternative that produces multiplanar
reformatted (MPR) images (White, 2000), allows clinicians to examine the region of interest in
any plane (axial, coronal, and sagittal), determine accurate measurements, and eliminate tissue
compression (Levin, 2016). It also permits the assessment of the exact position of the crown and
root apex (Crescini, 2002), and the degree and direction (buccal, lingual, mesial, or palatal) of
root dilaceration.
Although the effective radiation dose is significantly less than a conventional
computerized axial tomograph (CT), it is still higher than panoramic or periapical radiography.
CBCT radiation dose is 15 times less than that of a conventional CT, but 12 times more than an
average panoramic radiograph (Scarfe, 2006) and equal to a full mouth intraoral series
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(Avendanio, 1996; Danforth, 2000; Hatcher, 2004; Ludlow, 2006; Mah, 2003; Schulze, 2004; ).
CBCT devices emit on average 36.9-50.3 microsievert (μSv) of radiation dose, averaging 1.320
to 3.324-μSv for the mandible and 1.031 to 1.420 μSv for maxilla (Karatas, 2014).
The main criterion for taking any radiograph is that it is likely to have an influence on the
clinical management of the patient (Mattick, 1999). The probable clinical benefit must be
weighed against the potential risks. CBCT scans have demonstrated high accuracy and reliability
for root length measurements, compared to direct skull measurements (Lund, 2010) and
periapical radiographs. Gunst (2013) and Ponder et al (2013) demonstrate the value of a CBCT
in measuring external apical root resorption. 3D shape analysis using CBCT images allows
phenotypic characterization and can be used to evaluate root size and shape pre- and post-
treatment (Ahlbrecht and Ruellas, 2017).
Additional advantages of a CBCT include: excellent tissue contrast, the possibility of
displaying and arranging 3D data in personal computers, the various computer software for
image processing, and the low maintenance cost (Karatas, 2014). CBCT’s are great tools in
orthodontics for evaluating dental root inclination and torque, impacted teeth and intraoral
anomalies, skeletal asymmetries, the sinuses and nasopharyngeal airway, cleft lip and palate,
temporomandibular joint (TMJ) morphology, and hyperplastic growth (Cevidanes, 2006b;
Karatas, 2014). The superimposition methods involved compare cranial base structures voxel by
voxel, allowing for the calculation of the rotation and translation parameters between 2 time-
point images, the superimposition of 3D virtual models, and the measurement of the distances
between 3D model’s surfaces. Compared to periapical radiography, CBCT scans are equal in the
ability to detect periapical bone defects (Stravropoulos, 2007) and measure periodontal bone
levels and defects (Misch, 2006; Vandenberghe, 2007). Limited field-of-view CBCT units are
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also comparable to intraoral radiography in the detection of interproximal caries in unrestored
teeth (Akdeniz, 2006; Kalathingal, 2007; Neto, 2008; Tsuchida, 2007). CT scans with a voxel
size of .4mm are considered comparable to periapicals in terms of accuracy and reliability, while
allowing for a lower level of radiation. However, a voxel size of .2mm had less method error
(Sherrard, 2010). To avoid taking multiple diagnostic images, a cephalometric-like and
panoramic-like view, along with many other custom radiographic views can be rendered from
CBCT data (Sherrard, 2010).
3D virtual models can be built from a set of 300 or more axial cross-sectional slices from
the CBCT scan. The scanned images can be converted from DICOM (Digital Imaging and
Communications in Medicine) to a segmenting format. Segmentation is the process of outlining
the structural shape “visible in the cross-sections of a volumetric data set” (Cevidanes, 2006b).
Once segmentation is completed, a 3D graphic rendering of the volumetric object allows
exploration between voxels in the volumetric image (Cevidanes, 2006b). The automation of
image analysis procedures, such as 3D model construction, superimposition at various time
points, and surface distance calculations, will reduce the observer error (Cevidanes, 2006a).
Classification Design
Variation and categorization are matters significant to several different areas of study.
The purpose of categorization is “to identify generalizable classes of objects whose members can
be treated equivalently”(Iordan, 2016). Classification breaks down matter into groups and
subgroups, providing order and clarity. The basis for classification varies between subjects:
shape, color, size, absence of something, amount of something, the product of something, etc.
Some members are more representative of that concept than other members of the same category
and this typicality effect usually manifests as increased speed of recognition and lower error rates
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for category verification (Iordan, 2016). Highly typical members share the most features in
common with other members of the category and less features in common with other categories
in a similar semantic space. Typicality is responsible for category cohesion and distinctiveness
(Iordan, 2016).
Some theories claim that a person decides a test object belongs in a category “if it is
sufficiently similar to an abstract prototype of the category, or to known instances of the
categories. Other theories claim that similarity is determined by the match between the object's
features and those of the connection strengths that encode the categories” (Smith and Sloman,
1994). These theories are consistent with the evidence for "similarity-based categorization"
found in studies of artificial categories, involving schematic faces and complex geometric forms
(e.g., Estes, 1986; Nosofsky, 1986), and in studies that use natural categories (e.g., Smith &
Medin, 1981).
With advances in technology, there is a growing number of different ways to group and
divide. Many of the more recent classification systems were developed quantitatively, with a
rising amount of automated algorithms and the use of robotic recognition software. Automated
algorithms for classifying images based on appearance characteristics typically either extract
features from the image and use those features for classification or use the images directly as
input to the classification algorithm (Joshi et al, 2016). Point distribution models (PDMs) depict
“shape variability across a set of training structures by quantifying variations in the positions of
sets of corresponding landmark points (Cootes et al, 1995; Joseph et al, 2014)”. Statistical shape
analysis using a surface-based approach accurately models structural shapes and their possible
variations(Joseph et al, 2014; Shen, 2004).
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In determining the presence and subtypes of staphylomas in eyes with pathologic myopia,
OhnoMatsui et al 2014 used 3D MRI findings correlated with previously held subjective
funduscopic features to make the staphyloma classification useful for clinicians. Morphology
needs to be well defined in order to allow good agreement in clinical classification. In the scar
classification study by Kang et al in 2016, raters assessed scars on the same patient twice for
aggregate model analysis. They concluded that the subjective shape-based evaluations did not
readily yield strong agreement.
For neuronal classification based on morphological, psychological, or molecular
characteristics, quantitative methods using supervised and unsupervised classifiers have become
standard (Vasquez et. al, 2016). In this way, distinct neural subtypes are provided quantitatively
without bias. The morphometric differences between laboratories, the significant geometric
variation of individual neurons, the different extraction techniques (imaging, histology, and
reconstruction techniques), and inter-laboratory variability make morphological classification
difficult. Neuroinformatic tools, computational approaches, and available quality data are
improving the accuracy of classification. Thus, subjective classification by a neuroscientist is
important to improve models on curated data (Polavaram et al., 2014). Vazquez et al, 2016 stated
that supervised classifiers appear to be the best way to classify neurons according to previous
subjective neuron classification. Accounting for strictly the significant features to classify
morphologies increases the accuracy of classification algorithms. Considering each descriptor
type separately, and then combining them as the data sets increase is an appropriate strategy
(Vasquez et. al, 2016).
Archform classification is a subjective process based on clinical experience, occurring in
the mind of the orthodontist (Nouri, ). The arch is described by geometric shapes, such as
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catenary, parabolic, elliptical, and hyperbolic, but for simplicity, is usually categorized into 3
popular shapes: tapered, oval, and square. The subjective clinical assessments used to rank dental
archforms were generally in agreement at the extremes of tapered and square, with greater
variation for the intermediate ovoid shape (Arai, 2011). However, in the study done by Nouri et
al., semi-objective archform assessment (visual selection using software) yielded better results
than the fully automated approach.
Traditional descriptions on tooth morphology are mainly based on the qualitative
supervised classifications such as those of Gregory’s (1916) or Hellman’s (1928) with additional
two-dimensional (2D) quantifications (Lavelle et al., 1970). In those supervised classifications,
the classification criteria are predetermined according to morphologic characteristics that the
classifiers choose. In contrast to the supervised classification, unsupervised classification is an
analytical procedure based on clustering algorithms that seek out similarities between the pieces
of data and automatically develop classification labels (Lee et al., 2011b). Multivariate cluster
analysis, one of unsupervised classification, is a relatively new method in biomedical science
that can be used to interpret an entire set of data while preserving information about individuals.
In dentistry, this methodology has been successfully used to classify dental arch forms, tooth
size, and skeletal patterns free from practitioner bias (Kim et al., 2005).
Intra-Rater and Inter-Rater Reliability
Most of the reliability and accuracy of a classification stems from its ability to produce
reproducible results. In classification studies, the consistency of the assessor or rater for each
image or object in question is crucial. For example, if the rater classifies an object as round the
first-go-around and then as blunt the next time they view it, the chances of the descriptors being
reliable diminishes. The more agreement of the results among raters and between raters, the
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greater likelihood of creating a reliable classification scheme that can be used by the majority of
clinicians.
Testing the reliability of the assessment method involves reevaluation. Some studies had
the raters assess the same images or objects after a couple of weeks (Sameshima and Sinclair,
2004), certain amount of days (Witcher, 2016), later the same day (Kang, 2016), or within the
same round (Gelfand, 1983). Several studies reported a low kappa for subjective scoring of root
resorption (Smale, 2005) supporting that low agreement when scoring intact versus irregular root
contour (Goldson, 1975). The limitations for visual interpretation must always be considered.
In a study done by Witcher et al., examiners assessed 50 pairs of radiographs, both UAO
and DPT, for five different criteria. The results of each examiner were paired with one of the
other examiners who assessed the same 50 radiograph pairs. Pairing the results was done for
inter-examiner and intra-examiner reliability. The volunteer examiners were all qualified dentists
in their final year of a MSc/MOrtho postgraduate course. They were provided with a descriptive
index of root morphology (Fig. 2) and asked to record a finding of normal unless there was
evidence of abnormal root shape. If more than one of the incisor root shapes were visible on the
radiograph, they were asked to record the abnormal morphology in their own opinion and of
clinical concern. The following week, the same examiners were asked to repeat their
observations for 10 pairs of radiographs. Intra- and inter-examiner reliabilities were assessed
using Cohen kappa test. The agreement was calculated both for each individual root shape
anomaly and by inference for a combined “not normal shape” group. The intra-observer
reliability for identifying “normal” or “not normal” root shape was very good, but the reliability
for classifying specific root morphologies was low to moderate. The results showed difficulty in
distinguishing between pointed and pipette-shaped. However, the best intra-observer
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repeatability were bent (k=.553) and blunt (k=.468). More importantly, the interobserver
reliability for “normal” or “not normal” was low in this study. Previous studies also found
intraobserver reliability to be higher than interobserver reliability when assessing radiographs for
specific factors (Ferguson, 1992, Saunders, 2000 Holta, 2004)
The choice of examiner could be just as important as that of the test substance (Shaw,
1975). Clinical bias, education, experience, training, and other factors may contribute to the
interobserver variability of radiographic interpretation of dental abnormalities (Petrikowski,
1996). Most certified orthodontists have undergone at least twenty-four months of orthodontic
training following at least 36 months of dental education. The classification descriptors to
someone lacking such training may not be as comprehendible. Graduating orthodontic residents
have the educational background to unify such morphological descriptors for a classification
design.
Automatic continuous assessment of root morphology are expected to reduce inter-
investigator variability (Ahlbrecht and Ruellas, 2017).
Clinical Significance
Some authors have discussed different ways on how to prevent and reduce the chances of
OIRR, including: longer intervals between activations with light intermittent forces (Levander
and Malmgren, 1994), removable rather than a fixed appliance, periodic periapical radiographs
(Valladeros Neto, 2013)
The longevity of severely resorbed teeth might be compromised in patients susceptible to
marginal periodontal breakdown (Smale, 2005). In addition, teeth with abnormally short roots
might not be suitable as abutments if bridges are required in the future. If potential risk factors
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are identified, alternative treatment option can be considered: prosthetic solutions to close
spaces, releasing teeth from active archwires when possible, stripping instead of extracting and
fixation of resorbed teeth early (Brezniak & Wasserstein, 2002). If post-treatment radiographs
show resorption, follow-up radiographic examinations are recommended until the resorption has
stabilized (Weltman et al., 2010).

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Chapter 3: Hypothesis
Research Hypotheses
1. There is a high prevalence of abnormal root shapes in the population.
2. 3D Segmented Maxillary Incisors can be used to create a classification system.
3. A comprehensive subjective classification scheme for maxillary incisor root forms can be
constructed from 3D surface models using geometric shapes and common arch forms
used in architecture as criteria descriptors.
4. Maxillary incisor root shapes cannot be reliably classified subjectively.
Null hypotheses
1. There is not a high prevalence of abnormal root shapes in the population
2. 3D Segmented Maxillary Incisors can not be used to create a classification system.
3. A comprehensive subjective classification scheme for maxillary incisor root forms cannot
be constructed from 3D surface models using geometric shapes and common arch forms
used in architecture.
4. Maxillary incisor root shapes can be reliably classified subjectively.

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Chapter 4: Materials and Methods
This study was conducted at the Herman Ostrow School of Dentistry
of USC Department of Orthodontics. A database containing CBCT images was examined
alphabetically. Pre-treatment CBCT images of the maxillary anterior teeth of healthy patients
from the USC orthodontic clinic (-females and -males between the ages of 16 and 35 years) were
segmented using ITK-SNAP software. Patients were scanned between the years 2015-2017 with
a ORTHO KODAK 9000 scanner machine using 0.0765217 x 0.0765217 x 0.0765209 voxel
size. Cases were selected for the study if the following inclusion criteria were met:
1. Patients must have a pre-treatment CBCT of sufficient quality at the age of 16 years or
older.
2. Patients must have complete root formation of the maxillary central, lateral, and canine at
the time of the initial scan.
3. Subjects must be free from periapical pathology at the maxillary central, lateral, and
canine.
The exclusion criteria were:
1. Subjects with unerupted canines in close proximity to the root apex of the maxillary right
central or lateral incisor.
2. Subjects having undergone previous orthodontic treatment.
3. Subjects with previous endodontic or restorative treatment of the maxillary right central
or lateral incisor.
Thirteen patients who satisfied the above criteria were included in this study. The age, gender,
and ethnicity of each patient was collected. The patient age range was from 16 to 39 years.
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Patient ethnicity ranged: 5 Hispanics, 3 African Americans, 3 European American, and 2 Asian
American patients.
Maxillary central and lateral incisors of each patient were segmented in the ITK-SNAP
software and evaluated in the VAM software.

Figure 4. CBCT segmentation of maxillary left central incisor using ITK-SNAP software.
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Figure 5. CBCT segmentation of maxillary right central incisor using ITK-SNAP
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Figure 6. CBCT segmentation of maxillary left lateral incisor using ITK-SNAP software

Figure 7. 3D model of maxillary left lateral incisor (labial aspect) using VAM imaging software
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Figure 8. 3D model of maxillary left central incisor (labial aspect) using VAM imaging software

Figure 9. 3D model of maxillary right lateral incisor (mesial aspect) using VAM imaging software
Subgroups were created by grouping incisors that looked similar to one another.
Geometric shapes and common arch forms used in architecture were used to describe the 3D
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image from mesial, distal, labial, lingual, and apical aspects. Using shape-based object
recognition, subgroup criteria was described for each category:
Elliptical: From labial, palatal, distal, and mesial aspects, the apical third of the root
appears elliptical arched or basket-handle arched in shape. The apical aspect is a larger
circular shape than normal.
Pointed: The apical third of the root is a pointed arch shape in the labial, palatal, mesial,
and distal aspects. The apical aspect is a very small circular shape.
Flat/Trapezoidal: The labial and palatal aspects of the apical third of the root may
appear pointed or slightly parabolic in shape. However, from the mesial and distal
aspects, the apical third is trapezoidal and flat in shape. The apical aspect looks oval in
shape from the labial to palatal direction. Forms of this classification may also appear
trapezoidal or flat from the labial and palatal aspects.
Step/Divoted: From the labial or palatal aspects, the apical third of the root may appear
either pointed, parabolic, flat/trapezoidal, or elliptical in shape. However, from the mesial
and distal aspects, the apical third appears to have a full step or slope from labial to
palatal surface. It may appear to have two separate planes or slopes, or a single rampant
arch shape. The apical view shows a circle and an elliptical appearing arch shape outling
one side of the circle. Forms of this classification may also appear pointed, parabolic, flat,
or elliptical from the mesial and distal aspects with the step or slope visual from the labial
and lingual aspects.
Dilacerated: The apical third, middle third, or coronal third sharply bends more than 20
degrees in a distal, mesial, labial, or palatal direction. Mesial and distal (or surface
opposite direction of the dilaceration) is parabolic if dilacerarion is purely in one
direction (ie. not distolingual or mesiolingual).
Long/narrow: Root length is longer and more slender than normal. Labial, palatal,
mesial and distal aspects show parabolic arch shape, pointed arch, or rounded arch.
However, the apical third may also appear as flat or trapezoidal, elliptical, divoted, or
dilacerated as well.
Short: The entire root is of equal or lesser length than the crown. This root may appear to
be dilacerated, elliptical, rounded, pointed, flat or trapezoidal, parabolic, or devoted.

Wheeler’s definition of maxillary central incisor and maxillary lateral incisor are used to
describe the normal root shape for analysis. The average root of the maxillary central incisor is
cone-shaped with a relatively “blunt” apex from the labial. From the mesial and distal aspects, it
is cone shaped with usually a “bluntly rounded” apex. The length of the root is usually 2-3mm
longer than the crown length and tapers lingually (Ash and Nelson, 2003). Wheeler describes the
average maxillary lateral incisor root as narrow mesio-distally with a root length approximately
1.5 times the crown length. The root tapers evenly to a point approximately two-thirds of its
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length apically and often then curves sharply in the distal direction, ending in a pointed apex.
From the mesial aspect, the tooth appears cone-shaped (or straight) with a “bluntly rounded”
apex (Ash and Nelson, 2003).
All 52 incisors (4 incisors of 13 patients) were classified into subgroups on two separate
occasions prior to testing reliability of the classification scheme. Each subgroup was assessed to
ensure that each incisor fit into a single category.
To test the reliability of the classification criteria, five experienced orthodontists and six
first year orthodontic residents subjectively classified each maxillary central incisor and
maxillary lateral incisor as either long, short, or normal in length. Each category was described in
words and was accompanied by mesial, distal, labial, palatal, and apical views of the tooth root
that best fit the criteria in the category (Tables 1 and 2). With the criteria and the screenshot
views for the exemplar tooth for the category, assessors categorized each incisor as: normal,
elliptical, pointed, flat/trapezoidal, step/devoted, or dilacerated.
The assessors viewed the STL files of the maxillary central and lateral incisor of each
patient in the MeshLab 3D viewing software or Windows 10 3D viewer. Assessors were able to
zoom in and out and spin the 3D model for viewing all the root surfaces at all angles. They were
also able to view all four incisors of each patient at one time. The orthodontic residents were
shown the individual models as a group and were not able to individually manipulate the models
themselves but were able to view each model one by one from any angle.
One week following, the same group completed the classification of the same incisors to
test for method error and intra-rater reliability. The same measures applied in the second round.
The results from each assessor were analyzed. Inter-rater and intra-rater reliability were
evaluated. Agreement was tested with interclass and intraclass coefficients generated by Cohen’s
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Kappa test. Portney & Watkins (2000) suggested that ICC values of >0.75 represent good
reliability, ICC values of 0.50 – 0.74 represent moderate reliability, and ICC values of <0.50
represent poor reliability.
Table 1. Maxillary Central Incisor Root Classification Guide
Normal

Labial Palatal Mesial Distal Apical

Wheeler describes the average root of the maxillary central incisor as cone-shaped
with a relatively “blunt” apex from the labial. From the mesial and distal aspects, it is
cone shaped with usually a “bluntly rounded” apex.
The length of the root is usually 2-3mm longer than the crown length and tapers
lingually.

Elliptical

Labial Palatal Distal Mesial Apical

From labial, palatal, distal, and mesial aspects, the apical third of the root appears
elliptical arched or basket-handle arched in shape. The apical aspect is a larger
circular shape than normal.

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Pointed

Labial Palatal Mesial Distal Apical

The apical third of the root is a pointed arch shape in the labial, palatal, mesial, and
distal aspects. The apical aspect is a very small circular shape.

Flat,
trapezoidal

Labial Palatal Mesial Distal Apical

The labial and palatal aspects of the apical third of the root may appear pointed or
slightly parabolic in shape. However, from the mesial and distal aspects, the apical
third is trapezoidal and flat in shape. The apical aspect looks oval in shape from the
labial to palatal direction. Forms of this classification may also appear trapezoidal or
flat from the labial and palatal aspects.

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Step or Divot

Labial Palatal Mesial Distal Apical

From the labial or palatal aspects, the apical third of the root may appear either
pointed, parabolic, flat/trapezoidal, or elliptical in shape. However, from the mesial
and distal aspects, the apical third appears to have a full step or slope from labial to
palatal surface. It may appear to have two separate planes or slopes, or a single
rampant arch shape. The apical view shows a circle and an elliptical appearing arch
shape outling one side of the circle. Forms of this classification may also appear
pointed, parabolic, flat, or elliptical from the mesial and distal aspects with the step
or slope visual from the labial and lingual aspects.
Dilacerated

Labial Palatal Mesial Distal Apical

The apical third, middle third, or coronal third sharply bends more than 20 degrees in
a distal, mesial, labial, or palatal direction. Mesial and distal (or surface opposite
direction of the dilaceration) is parabolic if dilacerarion is purely in one direction (ie.
not distolingual or mesiolingual).

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Long, narrow

Labial Palatal Distal Mesial Apical

Root length is longer and more slender than normal. Labial, palatal, mesial and distal
aspects show parabolic arch shape, pointed arch, or rounded arch. However, the
apical third may also appear as flat or trapezoidal, elliptical, divoted, or dilacerated
as well.

Short

Labial Palatal Mesial Distal Apical

The entire root is of equal or lesser length than the crown. This root may appear to be
dilacerated, elliptical, rounded, pointed, flat or trapezoidal, parabolic, or divoted.

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Table 2. Maxillary Lateral Incisor Root Shape Classification Guide
Normal
(Wheeler)

Labial Palatal Mesial Distal Apical

Wheeler describes the average maxillary lateral incisor root as narrow mesio-
distally with a root length approximately 1.5 times the crown length. The root tapers
evenly to a point approximately two-thirds of its length apically and often then
curves sharply in the distal direction, ending in a pointed apex. From the mesial
aspect, the tooth appears cone-shaped (or straight) with a “bluntly rounded” apex.

Elliptical

Labial Palatal Mesial Distal Apical

From labial, palatal, distal, and mesial aspects, the apical third of the root appears
elliptical arched or basket-handle arched in shape. The apical aspect is a larger
circular or oval in shape than normal.

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Pointed

Labial Palatal Mesial Distal Apical

The apical third of the root is a pointed arch shape in the labial, palatal, mesial, and
distal aspects. The apical aspect is a very small circular shape.

Flat,
trapezoidal

Labial Palatal Mesial Distal Apical

The labial and palatal aspects of the apical third of the root may appear pointed or
slightly parabolic in shape. However, from the mesial and distal aspects, the apical
third is trapezoidal and flat in shape. The apical aspect looks oval in shape from the
labial to palatal direction. Forms of this classification may also appear trapezoidal
or flat from the labial and palatal aspects.

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Step or Divot

Labial Palatal Mesial Distal Apical

From the mesial or distal aspects, the apical third of the root may appear either
pointed, parabolic, trapezoidal, or elliptical in shape. However, from the labial and
palatal aspects, the apical third appears to have a full step or slope from mesial to
distal surface. It may appear to have two separate planes or slopes, or a single
rampant arch shape. The apical view shows a circle and an elliptical appearing arch
shape outling one side of the circle. Forms of this classification may also appear
pointed, parabolic, trapezoidal, or elliptical from the labial and palatal with the step
or slope visual from the mistal and distal aspects.

Dilacerated

Labial Palatal Mesial Distal Apical

Sharp bend in the apical third (or middle third; or coronal third) of the root in the
mesial or distal, palatal or labial direction at an angle greater than 20 degrees.
Mesial and distal (or surface opposite direction of the dilaceration) is parabolic if
dilacerarion is purely in one direction (ie. not distolingual or mesiolingual).
49 | P a g e

Long, narrow

Labial Palatal Mesial Distal Apical

Root length is longer and more slender than normal. Labial, palatal, mesial and
distal aspects show parabolic arch shape, pointed arch, or rounded arch. However,
the apical third may also appear as flat or trapezoidal, elliptical, divoted, or
dilacerated as well.
Short

Labial Palatal Mesial Distal Apical

The entire root is of equal or lesser length than the crown. This root may appear to
be dilacerated, elliptical, rounded, pointed, flat or trapezoidal, parabolic, or divoted.

50 | P a g e

Chapter 5: Results
Pre-treatment cone-beam computerized tomography (CBCT) images of maxillary incisors
were examined. Thirteen patients (4 males and 9 females; age 16 to 39) fit the inclusion criteria
and provided clear images for segmentation. There were 3 African American, 3 European
American, 5 Hispanic, and 2 Asian patients.
The majority of experienced orthodontists recognized 88.5% & 86.5% of the roots as
abnormal in shape. The results for each round showed: 9.6% & 5.77% elliptical, 25% & 13.5%
pointed, 11.5% & 1.9% flat/trapezoidal, 1.9% & 5.77% step or divoted, and 13.46 & 21.15%
dilacerated (71.4-81.8% of which were laterals). Assessors marked three or more categories for
67.3% & 61.5% of the virtual models and marked four or more categories for 23% & 21.15%.
There were three models (3UR2, 8UL1, and 8UR1) that all five assessors agreed on the first
round and two models (3UR2 and 8UL1) that all five assessors agreed on in the second round.
The models with unanimous shape agreement were classified as elliptical (3UR2) and pointed
(8UR1 and 8UL1). In the first round, five models (1UL2, 3UL1, 7UR2, 9UL2, and 9UR2) were
classified as either pointed or dilacerated with only one outlier assessor in disagreement. In the
second round, seven models (2UL1, 5UL1, 7UL2, 7UR2, 9UL2, 9UR1, and 9UR2) were
classified as either elliptical, pointed, or dilacerated with only one outlier assessor in
disagreement. There was two models (1UR2, 8UL2) in the first round in which nearly no one
agreed and one model (2UL2) in the second round in which nearly no one agreed. In terms of
length, the majority classified 53.8% & 55.77% of the incisors as normal, 25% & 21.15% as
short (84.6 & 90.9% of which are centrals), and 19.23 as long (80% of which are laterals). There
was unanimous agreement on length for ten models in the first round and for eleven models in
the second round.
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The majority (half or more than half) of orthodontic residents recognized 90.38% &
86.5% of the roots as abnormal. The results for each round showed: 17.3% & 15.38% elliptical,
25% & 26.9% pointed, 7.7% & 5.77% flat/trapezoidal, 3.85% & 3.85% step or divoted, 13.46%
& 11.54% dilacerated (100% of which are laterals). Assessors marked three or more categories
for 51.9% & 44.2% of the virtual models and marked four or more categories for 17.3% & 9.6%.
There were eight models (3UL1, 3UR2, 4UR1, 7UR2, 9UL2, 9UR1, 9UR2, and 12UL1) that all
six assessors agreed on the first round and four models (3UL1, 3UR2, 7UL2, and 7UR2) that all
six assessors agreed on in the second round. The models with unanimous shape agreement were
classified as elliptical (3UR2 and 9UR1), pointed (3UL1 and 12UL1), normal (4UR1), and
dilacerated (2UR2, 7UR2, 7UL2, 9UL2, and 9UR2). In the first round, there were five models
(5UL1, 6UR1, 7UL2, 8UL1, and 11UR1), that were classified as pointed or normal with only
one outlier assessor in disagreement. In the second round, there were ten models (1UR1, 3UL2,
5UL1, 8UL1, 8UR2, 9UL2, 9UR1, 9UR2, 11UR1, and 13UL2) that were classified as normal,
elliptical, pointed, and dilacerated. There was one model (5UR2) in the first round in which no
one agreed and two models (4UR2 and 10UR1) in the second round in which no one agreed. In
terms of length, the majority classified 51.9% & 48.1% of the incisors as normal, 15.38% &
11.54% as short (75% & 83.3% of which are centrals), and 21.5% & 28.8% as long (72.7% &
80% of which are laterals). There was a unanimous agreement on length for eleven models in the
first round and fifteen for the second round.
The Kappa intraclass agreement on length ranged from .453-.688 for the experienced
orthodontist and from .251-.473 for the orthodontic residents. The Kappa intraclass agreement on
shape ranged from .166-.625 for the experienced orthodontist and from .308-.452 for the
orthodontic residents.
52 | P a g e

Table 3. Maxillary Incisor Root Length and Shape Assessment from Experienced Orthodontists

A1 A1s A2 A2s B1 B1s B2 B2s C1 C1s C2 C2s D1 D1s D2 D2s E1 E1s E2 E2s
1 UL1 n normal n normal n normal s normal n point s point s point n point n step n step
UL2 n step n normal n point l normal n point n point n point n point n point l point
UR1 s dil n normal s step s normal s point s flat s flat n flat n dilacer n point
UR2 n point l normal l point l point l flat l flat n normal l point n flat l flat
2 UL1 s normal s normal s dilac s dilacer s ellip s dilacer s dilacer s dilacer n flat n dilacer
UL2 s normal s normal s normal n normal n point n point s flat s point s flat n flat
UR1 s dilacer s step s point s step s ellip s point s dilacer s dilacer n dilacer n flat
UR2 s dilacer s dilacer s dilacer n dilacer n point n flat s flat n flat s dilacer n dilacer
3 UL1 s point s point s point s dilacer s point s point s dilacer s dilacer n point n dilacer
UL2 n normal n normal n normal s normal n ellip s flat n ellip s flat n normal n step
UR1 s normal s point n dilacer n point s point s flat s dilacer s dilacer n normal n point
UR2 s ellip n ellip n ellip s ellip n ellip n ellip s ellip s ellip n ellip n ellip
4 UL1 n normal n normal n ellip n normal s ellip s ellip n point n point n ellip n ellip
UL2 n dilacer l dilacer l dilacer n point l ellip l flat s n point n ellip n ellip
UR1 n normal n normal n normal n normal s point s point n point n point n ellip n ellip
UR2 n step l dilacer l step l step l point l point n flat l flat n ellip n ellip
5 UL1 n normal s normal n normal n point s point s point n point n point l point n point
UL2 n normal n normal n normal n normal n ellip n flat n point n point n normal n step
UR1 n normalstep n step n normal s step s point s point n flat n point l normal n normal
UR2 n dilacer n dilacer l step n dilacer l point l flat n flat n flat n ellip n dilacer
6 UL1 s normal s normal s normal s point s point s flat s point s point s ellip s ellip
UL2 l dilacer n dilacer n step n point n point n flat n flat n flat n flat n step
UR1 s normal s normal s flat s flat s point s point s point s point s point n normal
UR2 l step n dilacerstep n point l step s flat l flat n flat n flat n flat n flat
7 UL1 l normal l normal l point l normal s point n point n point l point l normal l normal
UL2 l dilacer n dilacer l dilacer l dilacer l point n dilacer n flat n flat l dilacer l dilacer
UR1 l normalpoint l normalpoint l point n point n point n point n flat l point l normal l normal
UR2 l dilacer n dilacer l dilacer l dilacer n flat n dilacer n dilacer n flat l dilacer l dilacer
8 UL1 s point s point s point s point s point s point s point s point n point s point
UL2 l dilacer n dilacer n flat n normal n point n flat n flat n flat n normal n normal
UR1 s point s dilacer s point s point s point s flat s point s flat n point n point
UR2 n dilacer s dilacer n dilacer n dilacer n flat n flat n flat n flat n flat n dilacer
9 UL1 s step n normal n flat n normal s point s flat n point n point n step n step
UL2 l dilacer n dilacer l dilacer l dilacer n point n dilacer l dilacer l flat l dilacer l dilacer
UR1 n flat s flat n ellip s ellip s point s ellip n ellip n ellip l ellip l ellip
UR2 l dilacer n dilacer l dilacer l dilacer n point l dilacer l dilacer l flat l dilacer l dilacer
10 UL1 n normal n normal n normal n flat s point s flat n point n point n normal n normal
UL2 n dilacer n dilacer n dilacer n dilacer n flat n flat n flat n ellip n normal n normal
UR1 n normal n normal s normal s step s flat s point s point n ellip n flat n flat
UR2 n dilacer n dilacer n dilacer l dilacer n flat n flat n flat n ellip n flat n flat
11 UL1 n normal n normal n point n normal s point s flat n point n point n normal n normal
UL2 l flatstep l ellip l normal l normal l point l flat s flat n point l normal l normal
UR1 n normal n normal n dilacer n normal s point n point n point n point n normal n normal
UR2 l dilacer l dilacer l step l normal n point l flat l normal l flat l normal l normal
12 UL1 n point n normal n normal n normal s point n flat s point s point n point s point
UL2 n ellip n dilacer s ellip n ellip n point n point n point n flat n normal n ellip
UR1 n normalpoint n normal s point s normal s flat s flat s point s point n point s normal
UR2 n ellipdilac n dilacer n flat n dilacer n point n point n flat n flat n flat n dilacer
13 UL1 n normal n normal n point n normal s point s point n flat n point n flat n flat
UL2 l normal l normal l point l point l flat l flat n flat n point l point l normal
UR1 n dilacer n normal n point n normal n point s point n point n flat n flat n flat
UR2 n step l step l step l step n point n point l point l flat l point l step
53 | P a g e

Table 4. Maxillary Incisor Root Length and Shape Assessment from Orthodontic Residents

a1 a1s a2 a2s b1 b1s b2 b2s c1 c1s c2 c2s d1 d1s d2 d2s e1 e1s e2 e2s f1 f1s f2 f2s
1 UL1 n step n step s flat n point n ellip s ellip s step s step n step n point n step s step
UL2 l point l point l normal n point n normal n normal n point s normal l point n point n point n normal
UR1 n point n point n normal n point n point n point s dilacer n point n flat s normal l dilacer s point
UR2 n ellip l normal n normal n normal n flat l point l step l point n point l normal l point n point
2 UL1 s normal s normal s ellip s normal s ellip s ellip s dilacer s normal s ellip s normal s normal s ellip
UL2 n point s step n flat n normal s point s flat n flat s point s point n ellip n flat s flat
UR1 n step s flat n flat s flat n step s ellip n flat s ellip s dilacer s flat s flat s flat
UR2 l dilacer n dilacer s flat n dilacer s dilacer s dilacer s step s dilacer n flat n dilacer n dilacer n dilacer
3 UL1 l point n point n point n point s point n point s point n point s point n point s point s point
UL2 n normal n ellip l ellip n ellip s ellip n ellip s normal s ellip n normal n ellip n normal n normal
UR1 s flat s flat n norm n point n point n ellip s point n normal s flat n point n flat s point
UR2 s ellip n ellip n ellip n ellip s ellip s ellip s ellip n ellip s ellip s ellip s ellip s ellip
4 UL1 n ellip n ellip n normal l normal l ellip n ellip n normal n ellip n ellip n normal n ellip n ellip
UL2 l normal n flat l flat l normal n step n flat n ellip l normal n ellip l ellip n step s ellip
UR1 n normal l normal n normal l normal n normal n ellip s normal n normal n normal l normal n normal n ellip
UR2 l step l normal n ellip l normal n ellip n step l ellip n ellip l ellip n step l normal l ellip
5 UL1 s point l point n point l point l step n normal n point n point s point s point s point n point
UL2 n normal n normal l ellip n normal n step n flat s flat n flat n flat n ellip n normal s ellip
UR1 n normal l normal n normal l normal n normal n normal n normal l ellip n dilacer n step n step n normal
UR2 l ellip l dilacer l ellip l ellip l ellip n step l ellip l flat l ellip l ellip l normal n ellip
6 UL1 s point s dilacer n dilacer s normal s step n step s step s point s point s point s point s point
UL2 n dilacer l normal n flat n normal n step n step n step n step n dilacer n flat n flat n step
UR1 s normal s normal n point s normal s point n point s point s point s point s point n point s point
UR2 l flat n flat n flat n normal n step n flat s step n ellip l ellip n step n point n step
7 UL1 n point l normal l normal l normal n normal l normal l point l point l normal l ellip l point l point
UL2 l dilacer l dilacer l normal l dilacer l dilacer l dilacer l dilacer l dilacer l dilacer l dilacer l dilacer l dilacer
UR1 s point l normal l l point l normal l normal l step l point n point l normal l point l point
UR2 l dilacer l dilacer l dilacer l dilacer l dilacer l dilacer l dilacer l dilacer l dilacer l dilacer l dilacer l dilacer
8 UL1 n normal s point n point s point n point s ellip n point n point n point s point s point s point
UL2 l flat n flat l ellip n normal n step n flat l ellip l flat n dilacer n normal n normal n step
UR1 s point s point l normal s point s point s ellip s point s point s normal s dilacer n point s point
UR2 l ellip n ellip l ellip n ellip n dilacer s flat n ellip n ellip n dilacer s ellip n step n ellip
9 UL1 n flat n step n flat n normal n flat l flat n step n flat n step l point n flat n normal
UL2 l dilacer l dilacer n dilacer l dilacer n dilacer l point l dilacer l dilacer n dilacer l dilacer l dilacer l dilacer
UR1 n ellip s ellip n ellip n ellip n ellip n ellip n ellip n ellip n ellip n ellip n ellip n normal
UR2 l dilacer l dilacer l dilacer l dilacer l dilacer l dilacer l dilacer l point l dilacer l dilacer l dilacer l dilacer
10 UL1 n point n normal n point n point n ellip n normal n point n point n point n point n flat n point
UL2 n dilacer n ellip n normal n normal n point n ellip n point n point n dilacer n normal n normal n ellip
UR1 s ellip s flat n normal n flat n ellip n ellip n ellip n ellip n normal n normal n flat s normal
UR2 l flat n dilacer n flat n flat n step n flat s normal l step n dilacer n dilacer l flat n flat
11 UL1 n normal n point l point n normal l point n normal n point l point l normal n ellip n point n point
UL2 l step l step l ellip l ellip n ellip n point l flat n point l ellip l ellip l normal l ellip
UR1 n normal n normal n normal n point n normal n normal n normal l normal n normal n normal n point n normal
UR2 l dilacer n normal l normal l normal n dilacer l l step l point n dilacer s normal n dilacer l normal
12 UL1 s point l dilacer n point n point n point n point s point n point n point l dilacer n point n point
UL2 l dilacer s point n normal n ellip n dilacer s step n normal n normal l dilacer n step n normal n normal
UR1 s step n flat s normal n normal n point n flat s point n point s point n dilacer s point n normal
UR2 s dilacer l dilacer n n dilacer n dilacer n dilacer s step l step n dilacer s dilacer n normal l dilacer
13 UL1 n normal n normal l normal n normal n flat n ellip n step n point n normal n normal s point n point
UL2 l point l normal l point l point n point l point l step l point l ellip s point l point l point
UR1 n step n point l flat n flat n normal n normal n ellip l point n normal n point n flat n point
UR2 l step l point l point l point l step l step n point l normal n step l step l normal l point
54 | P a g e

Table 5. Frequency Data for Root Shape and Length Assessment in Experienced Orthodontists

Exp. Orthodontist
FIRST RUN SECOND RUN
Virtual Model Tooth Length Shape Length Shape
Normal Short Long Normal Elliptical Pointed Flat, Trapezoidal Step or Divot Dilacerated Normal Short Long Normal Elliptical Pointed Flat, Trapezoidal Step or Divot Dilacerated
1 UL1 4 1 2 2 1 3 2 2 2 1
UL2 5 4 1 3 2 2 3
UR1 1 4 1 1 1 2 3 2 2 1 2
UR2 3 2 1 2 2 5 1 2 2
2 UL1 1 4 1 1 3 1 4 1 4
UL2 1 4 2 1 2 3 2 2 2 1
UR1 1 4 1 1 3 1 4 1 1 2 1
UR2 1 4 1 1 3 4 1 2 3
3 UL1 1 4 4 1 1 4 2 3
UL2 5 3 1 ellip/dil 2 3 2 2 1
UR1 2 3 2 1 2 2 3 3 1 1
UR2 3 2 5 3 2 5
4 UL1 4 1 3 1 norm/ellip 4 1 2 2 1
UL2 2 1 2 3 1 ellip/dil 3 2 1 2 1 1
UR1 4 1 2 1 2 4 1 2 1 2
UR2 3 2 1 1 1 2 1 4 1 1 1 1 1
5 UL1 3 1 1 2 3 3 2 1 4
UL2 5 3 1 1 5 2 1 1 1
UR1 3 1 1 2 1 1 norm/step 3 2 1 2 2
UR2 3 2 1 1 1 1 1 4 1 2 3
6 UL1 5 2 1 2 5 1 1 2 1
UL2 4 1 1 2 1 1 5 1 2 1 1
UR1 5 1 3 1 1 4 2 2 1
UR2 3 1 1 1 3 1 3 2 3 1 dilacerstep
7 UL1 1 1 3 2 3 1 4 3 2
UL2 1 4 1 1 3 3 2 1 4
UR1 2 3 1 2 1 norm/point 2 3 1 3 normalpoint
UR2 2 3 1 4 3 2 1 4
8 UL1 1 4 5 5 5
UL2 4 1 1 1 2 1 5 2 2 1
UR1 1 4 5 1 4 2 2 1
UR2 5 3 2 4 1 2 3
9 UL1 3 2 2 1 2 4 1 2 1 1 1
UL2 1 4 1 4 2 3 1 4
UR1 3 1 1 3 1 1 1 3 1 4 1
UR2 1 4 1 4 1 4 1 4
10 UL1 4 1 3 2 4 1 2 1 2
UL2 5 1 2 2 5 1 1 1 2
UR1 2 3 2 1 2 3 2 1 1 1 1 1
UR2 5 3 2 4 1 1 2 2
11 UL1 4 1 2 3 4 1 3 1 1
UL2 1 4 2 1 1 flat/step 1 4 2 1 1 1
UR1 4 1 2 2 1 5 3 2
UR2 1 4 2 1 1 1 5 2 2 1
12 UL1 3 2 1 3 point/flat 3 2 2 2 1
UL2 4 1 1 2 1 1 5 2 1 1 1
UR1 2 3 3 1 norm/point 1 4 3 1 1
UR2 5 1 3 ellip/dil 5 1 1 3
13 UL1 4 1 1 2 2 4 1 2 2 1
UL2 1 4 1 2 2 1 4 2 2 1
UR1 5 3 1 1 4 1 2 1 2
UR2 2 3 3 2 1 4 1 1 3
55 | P a g e

Table 6. : Frequency Data for Root Shape and Length Assessment in Ortho Residents

Residents
FIRST RUN SECOND RUN
Virtual Model Tooth Length Shape Length Shape
Normal Short Long Normal Elliptical Pointed Flat, Trapezoidal Step or Divot Dilacerated Normal Short Long Normal Elliptical Pointed Flat, Trapezoidal Step or Divot Dilacerated
1 UL1 4 2 1 1 4 3 3 1 2 3
UL2 3 3 2 4 4 1 1 3 3
UR1 4 1 1 1 2 1 2 4 2 1 5
UR2 4 2 1 1 2 1 1 2 4 3 3
2 UL1 6 2 3 1 6 4 2
UL2 4 2 3 3 2 4 1 1 1 2 1
UR1 4 2 3 2 1 6 2 4
UR2 2 3 1 2 1 3 4 2 6
3 UL1 1 4 1 6 5 1 6
UL2 3 2 1 4 2 5 1 1 5
UR1 3 3 1 2 3 4 2 1 1 3 1
UR2 1 5 6 3 3 6
4 UL1 5 1 2 4 5 1 2 4
UL2 4 2 1 2 1 2 2 1 3 2 2 2
UR1 5 1 6 3 3 4 2
UR2 2 4 1 4 1 3 3 2 2 2
5 UL1 2 3 1 5 1 3 1 1 1 5
UL2 4 1 1 2 2 5 1 2 2 2
UR1 6 4 1 1 3 3 4 1 1
UR2 6 1 5 2 4 3 1 1 1
6 UL1 1 5 3 2 1 1 5 1 3 1 1
UL2 5 2 2 2 5 1 2 1 3
UR1 2 4 1 5 1 5 2 4
UR2 3 1 2 1 1 2 2 6 1 1 2 2
7 UL1 2 4 3 3 6 3 1 2
UL2 6 1 5 6 6
UR1 1 1 4 1 3 1 6 3 3
UR2 6 6 6 6
8 UL1 5 1 1 5 1 5 1 5
UL2 3 3 1 2 1 1 1 5 1 2 3 1
UR1 1 4 1 2 4 6 1 4 1
UR2 4 2 3 1 2 4 2 5 1
9 UL1 6 4 2 4 2 2 1 2 1
UL2 3 3 6 6 1 5
UR1 6 6 5 1 1 5
UR2 6 6 6 1 5
10 UL1 6 1 4 1 6 2 4
UL2 6 2 2 2 6 2 3 1
UR1 5 1 2 3 1 4 2 2 2 2
UR2 3 1 2 1 3 1 1 5 1 3 1 2
11 UL1 3 3 2 4 5 1 2 1 3
UL2 1 5 1 3 1 1 2 4 3 2 1
UR1 6 5 1 5 1 5 1
UR2 3 3 1 1 4 1 1 4 4 1
12 UL1 4 2 6 4 2 4 2
UL2 4 2 3 3 4 2 2 1 1 2
UR1 1 5 1 4 1 6 2 1 2 1
UR2 4 2 1 1 3 2 1 3 1 5
13 UL1 4 1 1 3 1 1 1 6 3 1 2
UL2 1 5 1 4 1 1 5 1 5
UR1 5 1 2 1 2 1 5 1 1 4 1
UR2 2 4 1 2 3 6 1 3 2
56 | P a g e

Chapter 6: Discussion
With the CBCT images, the roots of maxillary incisors can be viewed from any angle and
evaluated for classification. The classifications done from a panoramic, periapical, or upper
standard occlusal radiographs do not incorporate the information that can be gathered from a
three-dimensional image. Thus, a three-dimensional classification guide is necessary to
differentiate the different root shapes that three-dimensional imaging exposes.
When analyzing the reliability of this study, Portney & Watkins (2000) suggested
that ICC values of >0.75 represent good reliability, ICC values of 0.50 – 0.74 represent moderate
reliability, and ICC values of <0.50 represent poor reliability.
Assessor Bias
Each assessor has a different background and experience with root evaluation. While one
experienced orthodontist assessor studies roots consistently and has a strong research
background in root resorption, one does not practice clinically anymore and rarely evaluates
incisor root shape. Therefore, there is bias involved when it comes to comfort and familiarity
with analyzing root structure. Many of the experienced orthodontist assessors are used to
analyzing root shape in two-dimensional images and may have focused on the more labial views,
making hastier decisions based on prior experience in the field and knowledge of the two-
dimensional classification subgroups.
Some assessors reviewed the classification guidelines more thoroughly, while others
barely skimmed the guidelines prior to starting the classification process and rarely referred back
to the guideline during the classification. The purpose of the study was to use the familiarity and
comfort of basic geometric shapes and common arch forms in order to classify root shapes.
57 | P a g e

However, if the assessor does not study or refer to the guidelines, the results will not reflect the
strength of the designed guidelines and classification descriptors.
The orthodontic residents have less experience looking at incisor roots and had minimal
knowledge of previously formulated classification subgroups for incisor root shape.
Analysis of Incisor Root Length Assessment Results
The assessors subjectively identified almost half of the maxillary incisor roots to be
abnormal in root length. The experienced orthodontists identified almost a quarter of the roots as
short and 20% as long. The orthodontic residents identified more long roots (21.5% & 28.8%)
than short roots. However, in both groups, the short roots were more prevalent in maxillary
central incisors and the long roots were more prevalent in maxillary laterals.
Evaluating the segmented incisor root length using 3D imaging proved to be challenging
with a low interclass agreement. For the experienced orthodontists, there was unanimous
agreement on length for eleven models in the first round and fifteen for the second round. For the
orthodontic residents, there was unanimous agreement on length for ten models in the first round
and for eleven models in the second round. By averaging all the Kappas from the pairwise
comparisons (Light, 1971), the raters were compared and interclass agreement found. For the
experienced orthodontists, there was a poor agreement with a kappa=.3405. For the orthodontic
residents, there was also a poor agreement with a kappa= .339.
Analyzing root length for the segmented incisors individually as opposed to in relation to
adjacent teeth or bone level may account for the lack of agreement between assessors and the
low intraclass agreement. Without seeing the root in relation to any other structures, the assessors
may have relied on comparing the length of each incisor to the root length of the previously
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evaluated one. For example, if the first incisor was longer than normal and the second one was
normal, it may be difficult not to rate the first tooth as normal and the second as short. For the
segmented teeth, there is no line or color differentiation from incisor crown to incisor root.
Therefore, it is difficult to see exactly where the crown stops and root begins. If the difference
between the definition of long, normal, and short is in relation to crown height, it requires a good
eye to measure length subjectively.
The capability for rotation along the x, y, and z axes also created a challenge when
evaluating length. This may be the cause of a lot of the intraclass disagreement in this study. A
few of the assessors rated a tooth as short in one round and long in the other. The intraclass
reliability for root length was moderate to poor for the experienced orthodontists, with kappa
ranging from .453 to .688. However, the intraclass reliability for the orthodontic residents was
poor, with kappa ranging from .251-.473. Therefore, the perspectives in which they viewed the
incisor from one round to the next were so different that the assessors disagreed with themselves
most of the time.
Analysis of Incisor Root Shape Assessment Results
The segmented maxillary incisors provided clear 3D models to derive classification
criteria involving basic geometric shapes and common arch forms. However, there was
disagreement between the assessors in terms of shape classification for the majority of the
incisors. For the experienced orthodontists, there was only a unanimous agreement for 5.7%
(15.4% if removing single outlier) of the incisors in the first round and 3.8% (17.3% if removing
single outlier) of the incisors in the second round. For the orthodontic residents, there was a
unanimous agreement for 15.4% (25% if removing single outlier) in the first round and 7.7%
(27% if removing single outlier) in the second round.
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In the experienced orthodontist group, the shapes that had unanimous agreement were
elliptical and pointed. In the orthodontic resident group, the shapes that had unanimous
agreement were elliptical, pointed, normal, and dilacerated. The experienced orthodontists
identified more pointed and dilacerated roots than any other root shape form. The orthodontic
residents identified more pointed and elliptical roots than any other root shape form. The
category in which the assessors used the least was the step or divoted, followed by
flat/trapezoidal. Two experienced orthodontists (C&D) never used the step or divoted subgroup.
One experienced orthodontist (C) never used the normal category, while one experienced faculty
(D) used it only in one of the two rounds.
The intraclass agreement on shape ranged from .166-.625 (low to moderate) for
experienced orthodontists and from .308-.452 (low) for orthodontic residents. Therefore,
categorizing the majority of the incisors proved difficult. Incisors that were identified as pointed
were also categorized as normal, and sometimes elliptical. Incisors that were identified as normal
were also categorized as dilacerated, especially the laterals. There were incisors that were
identified in three or more different categories, which means that categories or category
descriptors need to be revised. Each tooth should be able to fit in a category and should be able
to be identified easily. There will need to be more differentiators between subgroups, such that
assessors can distinguish pointed from normal and elliptical. This also calls for a clearer and
more definitive description of the normal root shape.
Method Error
A more standardized and controlled method for assessing root shapes would provide
better information when testing for reliability. Using the 3D viewing software allowed assessors
to manipulate the image, making it difficult at times to determine the true axis for comparison.
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For example, some assessors evaluated roots as short because it was not clear when the tooth’s
center of rotation was straight 90 degrees to the y-plane. The tooth rotated along the x-, y-, and z-
axes. This may also account for the low intraclass agreement. Depending on the angle the tooth
is viewed from, the root appears differently in shape and in length.
Creating smooth and clearly identifiable 3D models is important for subjective
categorization. The segmentations were completed by a single person and the teeth were
evaluated for category creation by the same individual. Taking the average of the teeth that are
subjectively placed in the one category will incorporate and represent more of the incisors in that
category. Not every tooth looks exactly like the one placed in the guideline for their category.
For those that only looked at the representative images for each category, rather than reading the
descriptors may not have been able to classify the teeth that did not look exactly like the
representative incisor on the guide provided.
The sample size of this study was not large enough to get a strong representation of the
population. More incisors will allow the study to evaluate the different root shapes and develop
an average root shape to call standard and represent normal. The “new normal” can then be
described using basic geometric forms and arch forms, which may allow assessors to better
distinguish normal from the abnormal shapes. Creating categories using a focus group or group
of individuals to view and describe the distinguishing features of each tooth may increase the
reliability of the classification scheme by gathering and incorporating the different perspectives.
Future of This Study
Creating a comprehensive and clear 3D Subjective Classification guideline is very
important so that each tooth can clearly be placed into a single category. A group, rather than an
61 | P a g e

individual, will be involved in surveying each tooth and creating categories using geometric
shapes and arch forms. A set of a few incisors, rather than just one, that meet the criteria of the
category will be given to the assessors in a guide to represent the category.
The method for testing the reliability of the subgroups needs to be more systematic. The
guidelines and criteria need to be verbally explained to every assessor, eliminating the possibility
of assessors not being informed of the criteria prior to classification. The amount of time allotted
to evaluate each tooth and the method for viewing the models in 3D should be the same for each
assessor. The way in which the model is brought up on the screen and rotated should be exactly
the same for every assessor for both rounds. A video for each incisor will limit the different
biases of each assessor, making them view each aspect of the tooth before making a hasty
decision. After creating a more specific and inclusive classification guideline, the next stage
involves classifying the roots in Dolphin Imaging software to imitate a more clinical evaluation
setting.

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Conclusion
CBCT imaging is becoming a more popular tool for orthodontic diagnosis and treatment
planning. With two-dimensional radiology, the overlying structures, artifacts, and image
distortion make it difficult to evaluate the true root form of the dentition. CBCT imaging is an
effective method for detecting abnormal root morphology that present a potential risk for root
resorption. Once a classification system is established, the subgroups can be tested for
prevalence and root resorption susceptibility.
From this study the following conclusions were:
1. Three-dimensional imaging allowed assessors to identify approximately 87.95% of the
maxillary incisors as abnormal.
2. The segmented maxillary incisors provided clear 3D models to derive classification
criteria involving basic geometric shapes and common arch forms.
3. There was a low to moderate intraclass agreement for length amongst the experienced
orthodontists and a low intraclass agreement for length amongst the orthodontic residents.
4. There was a low to moderate intraclass agreement for shape amongst the experienced
orthodontists and a low intraclass agreement for shape amongst the orthodontic residents.
5. Assessor responses regarding root shape showed a strong lack of agreement for the
majority of the incisors. Assessors had difficulty differentiating normal from pointed and
elliptical. Assessors had difficulty differentiating normal from dilacerated, specifically
for the maxillary lateral incisors.

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72 | P a g e

Appendix

Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
A1 * A2 52 100.0% 0 0.0% 52 100.0%

A1 * A2 Crosstabulation
Count

A2
Total l n s
A1 l 5 7 0 12
n 4 20 3 27
s 0 3 10 13
Total 9 30 13 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .453 .110 4.510 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
B1 * B2 52 100.0% 0 0.0% 52 100.0%

B1 * B2 Crosstabulation
Count
73 | P a g e

B2
Total l n s
B1 l 11 3 0 14
n 3 17 5 25
s 0 3 10 13
Total 14 23 15 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .581 .097 5.868 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.

Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
C1 * C2 52 100.0% 0 0.0% 52 100.0%

C1 * C2 Crosstabulation
Count

C2
Total l n s
C1 l 6 1 0 7
n 2 16 3 21
s 1 3 20 24
Total 9 20 23 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
74 | P a g e

Measure of Agreement Kappa .688 .088 6.606 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
D1 * D2 52 100.0% 0 0.0% 52 100.0%

D1 * D2 Crosstabulation
Count

D2
Total l n s
D1 l 4 0 0 4
n 4 25 1 30
s 0 6 12 18
Total 8 31 13 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .621 .101 5.881 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
E1 * E2 52 100.0% 0 0.0% 52 100.0%

E1 * E2 Crosstabulation
75 | P a g e

Count

E2
Total l n s
E1 l 11 2 0 13
n 2 30 3 35
s 0 3 1 4
Total 13 35 4 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .598 .110 5.306 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
a1 * a2 52 100.0% 0 0.0% 52 100.0%

a1 * a2 Crosstabulation
Count

a2
Total l n s
a1 l 10 8 1 19
n 5 12 4 21
s 4 2 6 12
Total 19 22 11 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .286 .108 2.865 .004
76 | P a g e

N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
b1 * b2 52 100.0% 0 0.0% 52 100.0%

b1 * b2 Crosstabulation
Count

b2
Total l n s
b1 l 11 8 1 20
n 6 18 4 28
s 0 3 1 4
Total 17 29 6 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .251 .115 2.269 .023
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
c1 * c2 52 100.0% 0 0.0% 52 100.0%

c1 * c2 Crosstabulation
Count
77 | P a g e

c2
Total l n s
c1 l 5 4 0 9
n 6 23 5 34
s 0 4 5 9
Total 11 31 10 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .324 .122 3.178 .001
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
d1 * d2 52 100.0% 0 0.0% 52 100.0%

d1 * d2 Crosstabulation
Count

d2
Total l n s
d1 l 11 2 0 13
n 6 12 3 21
s 2 9 7 18
Total 19 23 10 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .362 .102 3.814 .000
N of Valid Cases 52

78 | P a g e

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
e1 * e2 52 100.0% 0 0.0% 52 100.0%

e1 * e2 Crosstabulation
Count

e2
Total l n s
e1 l 6 5 1 12
n 8 16 5 29
s 0 4 7 11
Total 14 25 13 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .283 .113 2.843 .004
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
f1 * f2 52 100.0% 0 0.0% 52 100.0%

f1 * f2 Crosstabulation
Count

f2 Total
79 | P a g e

l n s
f1 l 10 3 1 14
n 2 19 8 29
s 0 3 6 9
Total 12 25 15 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .473 .104 4.818 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
A1s * A2s 52 100.0% 0 0.0% 52 100.0%

A1s * A2s Crosstabulation
Count

A2s
dilacer dilacerstep ellip flat normal normalpoint
A1s Dilacer 13 0 0 0 2 0
Ellip 1 0 1 0 0 0
Ellipdilac 1 0 0 0 0 0
Flat 0 0 0 1 0 0
Flatstep 0 0 1 0 0 0
Normal 0 0 0 0 17 0
normalpoint 0 0 0 0 1 1
Normalstep 0 0 0 0 0 0
Pointed 1 0 0 0 2 0
Step 1 1 0 0 2 0
Total 17 1 2 1 24 1

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A1s * A2s Crosstabulation
Count

A2s
point step

A1s dilacer 0 1 16
ellip 0 0 2
ellipdilac 0 0 1
Flat 0 0 1
flatstep 0 0 1
normal 1 0 18
normalpoint 0 0 2
normalstep 0 1 1
pointed 2 0 5
step 0 1 5
Total 3 3 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .166 .037 4.976 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.

81 | P a g e

Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
B1s * B2s 52 100.0% 0 0.0% 52 100.0%

B1s * B2s Crosstabulation
Count

B2s
Total dilacer ellip flat normal point step
B1s dilacer 9 0 0 1 2 0 12
ellip 0 3 0 1 0 0 4
flat 1 0 1 2 0 0 4
82 | P a g e

normal 0 0 1 7 2 2 12
point 1 0 0 6 5 2 14
step 1 0 0 2 1 2 6
Total 12 3 2 19 10 6 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .391 .088 5.729 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.

Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
C1s * C2s 52 100.0% 0 0.0% 52 100.0%

C1s * C2s Crosstabulation
Count

C2s
Total dilacer ellip flat point
C1s ellip 1 2 3 1 7
flat 1 0 7 1 9
point 3 1 14 18 36
Total 5 3 24 20 52

83 | P a g e

a. Kappa statistic cannot be computed. It requires
a two-way table in which the variables are of the
same type.

84 | P a g e

Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
D1s * D2s 52 100.0% 0 0.0% 52 100.0%

D1s * D2s Crosstabulation
Count

D2s
Total dilacer ellip flat point
D1s dilacer 4 0 3 0 7
ellip 0 2 1 0 3
flat 0 2 10 6 18
normal 0 0 1 1 2
point 0 1 4 17 22
Total 4 5 19 24 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .448 .100 4.967 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.

85 | P a g e

Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
E1s * E2s 52 100.0% 0 0.0% 52 100.0%

E1s * E2s Crosstabulation
Count

E2s
Total dilacer ellip flat normal point step
E1s dilacer 5 0 1 0 1 0 7
ellip 1 7 0 0 0 0 8
flat 3 0 7 0 0 1 11
86 | P a g e

normal 0 1 0 10 1 2 14
point 1 0 0 3 5 1 10
step 0 0 0 0 0 2 2
Total 10 8 8 13 7 6 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .625 .077 9.868 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.

87 | P a g e

Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
a1s * a2s 52 100.0% 0 0.0% 52 100.0%

a1s * a2s Crosstabulation
Count

a2s
Total dilacer ellip flat normal point step
a1s dilacer 6 1 0 2 1 0 10
ellip 1 4 1 1 0 0 7
flat 1 0 3 0 0 1 5
normal 0 1 1 7 2 0 11
point 2 0 0 4 5 1 12
step 0 0 2 1 2 2 7
Total 10 6 7 15 10 4 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .413 .084 6.480 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.

88 | P a g e

Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
b1s * b2s 52 100.0% 0 0.0% 52 100.0%

b1s * b2s Crosstabulation
Count

b2s
Total dilacer ellip flat normal point
b1s dilacer 4 0 0 1 0 5
ellip 0 6 0 4 0 10
flat 1 0 3 5 1 10
norm 0 0 0 0 1 1
normal 1 1 1 9 4 16
89 | P a g e

point 0 0 0 2 8 10
Total 6 7 4 21 14 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .452 .090 6.444 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.

Case Processing Summary

Cases
90 | P a g e

Valid Missing Total
N Percent N Percent N Percent
c1s * c2s 52 100.0% 0 0.0% 52 100.0%

c1s * c2s Crosstabulation
Count

c2s
Total dilacer ellip flat normal point step
c1s dilacer 6 0 1 0 1 1 9
ellip 0 7 0 1 1 2 11
flat 0 1 1 0 1 0 3
normal 0 1 0 6 0 0 7
point 0 4 2 1 5 0 12
step 0 1 5 1 0 3 10
Total 6 14 9 9 8 6 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .445 .081 7.327 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.

91 | P a g e

Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
d1s * d2s 52 100.0% 0 0.0% 52 100.0%

d1s * d2s Crosstabulation
Count

d2s
Total dilacer ellip flat normal point step
d1s dilacer 3 0 0 1 2 0 6
ellip 0 5 2 1 1 0 9
flat 0 1 1 0 2 0 4
normal 0 3 0 3 0 1 7
92 | P a g e

point 0 0 0 3 11 0 14
step 1 1 1 0 6 3 12
Total 4 10 4 8 22 4 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .375 .082 5.867 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.

93 | P a g e

Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
e1s * e2s 52 100.0% 0 0.0% 52 100.0%

e1s * e2s Crosstabulation
Count

e2s
Total dilacer ellip flat normal point step
e1s dilacer 6 1 2 3 0 2 14
ellip 0 5 0 2 1 2 10
flat 1 1 0 1 1 0 4
normal 1 3 0 4 1 0 9
point 2 1 0 2 7 0 12
step 0 0 0 0 2 1 3
Total 10 11 2 12 12 5 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .308 .082 4.626 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.

94 | P a g e

Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
f1s * f2s 52 100.0% 0 0.0% 52 100.0%

f1s * f2s Crosstabulation
Count

f2s
Total dilacer ellip flat normal point step
f1s dilacer 5 0 0 1 1 0 7
ellip 0 2 0 1 0 0 3
flat 0 0 3 2 3 1 9
normal 1 7 0 2 1 1 12
95 | P a g e

point 0 0 0 3 13 1 17
step 0 2 0 1 0 1 4
Total 6 11 3 10 18 4 52

Symmetric Measures

Value
Asymptotic
Standard Error
a
Approximate T
b

Approximate
Significance
Measure of Agreement Kappa .374 .082 5.836 .000
N of Valid Cases 52

a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.

Abstract (if available)

Abstract Background: Orthodontically induced root resorption has been a growing concern in the field of orthodontics. There is a higher prevalence of root resorption in the maxillary anterior teeth, especially the maxillary lateral incisor. The root of the maxillary lateral is most commonly abnormally shaped. Several studies in the literature have contradictory reports for the different root shapes of maxillary incisors and the susceptibility of root resorption. There are several different subgroups for classifying roots across studies. Most of the abnormal root shape studies use 2-dimensional images. Three dimensional CBCT imaging provides a better diagnostic tool for evaluating root shape and root resorption. ❧ Purpose: The purpose of this study was to determine the following: 1. The prevalence of abnormal root morphology in orthodontic subjects. 2. If a comprehensive subjective classification scheme for maxillary incisor root forms can be constructed from 3D surface models using basic geometric shapes and common arch forms used in architecture for criteria descriptors. 3. The reliability of a subjective 3D classification scheme for diagnosis of abnormal root forms. 4. The clinical significance of a subjective classification method for common use in the office. ❧ Materials and Methods: This study was conducted at the Herman Ostrow School of Dentistry of USC Department of Orthodontics. A database of pre-treatment cone-beam computed tomography (CBCT) images was examined alphabetically. Cases were selected for the study based on a set of selection criteria. ITK-SNAP software was used to create 3D models of the maxillary centrals and laterals of each patient. All incisors were subjectively categorized based on geometric shapes and common arch forms from the study of architecture. Provided with the classification qualifications for each category, resident orthodontists and practicing orthodontists evaluated the 3D models and subjectively categorized each of them. ❧ Results: Of the 60 records reviewed, 13 patients were found with the inclusion criteria (4 males and 9 females, sex ratio 1:2.25) with an age range of 16 to 39 years. The prevalence of abnormally shaped roots in this sample was 87.5% from the experienced orthodontists and 88.4% from orthodontic residents. Experienced orthodontists identified 81.7% of the incisors as being normal in length and the orthodontic residents identified 50% of the incisors as normal in length. Assessor intraclass agreement on length ranged from .453-.688 for experienced orthodontists and .251-.473 for orthodontic residents. Assessor intraclass agreement on shape ranges from .166-.625 for the experienced orthodontist and from .308-.452 for the orthodontic residents. ❧ Conclusion: There is a high prevalence of identifiable abnormally shaped maxillary incisor roots. The classification scheme for maxillary incisor root forms was constructed using basic geometric shapes and arch forms and enabled assessors to categorize roots. However, there was a low to moderate inter- and intra-class agreement for classifying incisors based on root length and shape. Therefore, the subjective incisor root classification criteria as presented in this study is not reliable and needs to be revised in order for each tooth to fall into a single category. More importantly, the method for testing the reliability of the categories needs to be more systematic and regulated for each assessor in each round.

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Asset Metadata

Creator Clayton, Courtney M. (author)

Core Title Classification of 3D maxillary incisor root shape

School School of Dentistry

Degree Master of Science

Degree Program Craniofacial Biology

Publication Date 03/07/2018

Defense Date 02/23/2018

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag 3D root analysis,maxillary incisor classification,OAI-PMH Harvest,root form,root resorption,root shape

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Sameshima, Glenn (committee chair), Grauer, Dan (committee member), Paine, Michael (committee member)

Creator Email cmclayto@gmail.com,cmclayto@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-484689

Unique identifier UC11266785

Identifier etd-ClaytonCou-6087.pdf (filename),usctheses-c40-484689 (legacy record id)

Legacy Identifier etd-ClaytonCou-6087.pdf

Dmrecord 484689

Document Type Thesis

Rights Clayton, Courtney M.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA