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3D assessment of virtual bracket removal for modern orthodontic retainers: a prospective clinical study

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3D assessment of virtual bracket removal for modern orthodontic retainers: a prospective clinical study

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3D assessment of virtual bracket removal for modern orthodontic retainers: A prospective
clinical study

by

Kaitlin Marsh, DDS

A Thesis Presented to the
Faculty of the USC Herman Ostrow School of Dentistry
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
Master of Science

Craniofacial Biology

May 2020

2
Acknowledgements

I would like to thank Dr. Andre Weissheimer for his guidance in designing this study. His
expertise and teaching assisted me in executing this study. Additionally, I acknowledge and
commend Dr. Alexandra Chamberlain for laying the groundwork with her research for this
clinical study

I would also like to thank Dr. Kevin Yin for his aid in statistical analysis of the data.

I would also like to thank Dr. Zoey Gutierrez for her dedication and time in helping with
measurements for this study. I also would like to thank Dr. Glenn Sameshima and Linda for
helping me set up communication with two outside labs.

Finally, I thank my co-residents for helping me with identifying subjects for this study.

3
Table of Contents

1. Abstract 4
2. Introduction 5
3. Materials and Methods 9
a. Sample 9
b. Protocols 11
c. Superimposition 12
d. Measurements 12
e. Statistical Analysis 13
4. Results 15
5. Discussion 18
6. Conclusions 26
7. References 27
8. List of Tables 31
9. List of Figures 40
10. Figure Legends 48
11. Appendix 50

4
Abstract
Introduction: Computer-aided design and manufacturing of orthodontic retainers from digitally
debonded models can be used to facilitate same-day delivery. The purpose of this prospective
clinical study was to evaluate the accuracy of virtual bracket removal (VBR) techniques using
two orthodontic laboratories and the in-office VBR protocol. The objective was to compare the
accuracy of VBR from the two laboratories and the in-house VBR software with conventional
bracket removal to provide evidence-based support of clinical use of different VBR systems.
Methods: In this prospective study, the sample consisted of intra-oral scans of 20 patients. There
were four groups compared (all subject participants will be included in each group). Group 1 was
a control group of post-bracket removal scans/pre-bonding scan. Group 2 consisted of post-VBR
scans from in-office, using a previously established protocol with Meshmixer
TM
software. Group
3 consisted of post- VBR scans from ODL Orthodontic Lab (Buffalo, NY) and Group 4
consisted of post-VBR scans from Neolab (Andover, MA). Scans were taken twice in the same
time point before and after bonding or before and after debonding. The scans with and without
brackets were obtained on the same day for each patient using the 3Shape Trios scanner. The
scans without brackets were used as a control group. The second group of scans underwent VBR
using an in-house VBR software, Meshmixer
TM
, using the USC VBR protocol previously
established. The scans with brackets were sent to two orthodontic labs, ODL Laboratories, and
Neolab, where VBR was performed and the STL scans obtained after VBR. The virtually
debonded models were superimposed onto the control models, and 3D Euclidean distances
between surface points of superimposed models were calculated for comparative analysis of
surface changes due to VBR. Surface changes were expressed via color mapping using VAM
software. One-way ANOVA was used to detect the potential differences between three VBR
protocols/labs, separate teeth and tooth segments (incisors, canines/premolars, and first molars).
Statistical differences between the test groups were further analyzed with Scheffé’s post hoc test
(⍺ = 0.05).
Results: Intra- and inter-operator reliability were determined to be high (>0.9). The accuracy of
VBR using the Meshmixer
TM
protocol did not differ significantly from VBR protocols done by
the two labs. However, there was a statistically significant difference (P< 0.05) between the two
labs, with ODL showing least accuracy. This finding is likely not clinically significant. When
comparing the study groups to the control, there was a statistically significant difference between
the three tooth segments (incisors, canines/premolars, and first molars), with VBR of the first
molars and second premolars showing the least accuracy.
Conclusions: VBR using the Meshmixer
TM
protocol is comparable to VBR using Neolab or
ODL Laboratories. VBR accuracy increases from posterior teeth to anterior teeth. VBR using
any of the three protocols is clinically acceptable for computer-aided design and manufacturing
of retainers.

5
Introduction

Advances in 3D digital technology and computer-aided design and computer-aided
manufacturing (CAD-CAM) has revolutionized dentistry, especially within the specialty of
orthodontics. Digital technology began to make its way into the field of orthodontics in 1974
(Tarraf and Ali, 2018) with the introduction of computerized scheduling. 3D imaging
technologies offer many advantages, including the ability to 3D print for customized appliance
fabrication (Jheon et al., 2017). Digital scanning, which was introduced in dentistry in the mid-
1980s (Gan et al., 2016; Vaid, 2018) is rapidly replacing conventional impressions for
orthodontic occlusal records. In the past, dental models were made using alginate impressions
and plaster casts. Advantages of digital scanning include simplicity and accuracy, longevity
(Hazeveld et al., 2014), reduced patient discomfort, elimination of impression material in
inventory, reduced storage issues, and minimization of cross-contamination (Vasudavan et al.,
2010). With 3D printing gaining traction in the orthodontic community, many offices have in-
office digital laboratories for the fabrication of orthodontic models and appliances in an effort to
increase efficiency and reduce the number of appointments for patients (Groth et al., 2018).
Many orthodontists have incurred the additional costs of intraoral scanners and 3D printers
because of these advantages. The high cost of intraoral scanning can quickly be recovered by
reduced overhead and increased practice efficiency (Kravitz et al, 2014). With the rapid
progression of 3D printing, it will be possible to make most appliances in office with in-house
printers (Tarraf and Ali, 2018).

In order to perform the first step of digital workflow, an accurate digital scan must be taken with
an intraoral scanner. An accurate scan is crucial for the use of digital models for diagnosis,
6
treatment planning, and fabrication of orthodontic appliances. The accuracy of digital models is
especially critical for fabrication of orthodontic retainers. In recent years, in vivo comparison
studies between conventional impressions and digital scans have shown that digital impressions
accomplish equal and higher precision than some conventional impression materials (Ender and
Mehl al., 2016). Furthermore, digital models have been shown to be comparable with plaster
models if not more accurate (Tarraf and Ali, 2018) using both linear and angular measurements
as well as shell-shell deviation and arch-registration measurements (Burzynski et al., 2018;
Sousa et al., 2012; Wiranto et al., 2013). Accuracy of 3D printed orthodontic casts has been
shown to be well below the reported guidelines for accuracy of orthodontic casts, which are 300-
500µm (Cole et al., 2019). Although there are many types of intraoral scanners in the market that
possess trueness and precision sufficient for orthodontic applications (Claus et al., 2018), the
3Shape TRIOS scanner has been shown to be more precise (Michelinakis et al., 2019; Jung et al.,
2016; Anh et al., 2015) and can be easier to use (Lim et al, 2017) when compared to other
scanners in scanning complete arches (Claus et al., 2018). TRIOS and iTero scanners have
shown good trueness and precision even when scanning arches with bonded buccal brackets
(Park et al., 2016).

With digital workflows rapidly replacing conventional workflows in the orthodontic office,
digital fabrication of appliances in-house and by many orthodontic laboratories is beginning to
replace conventional methods. In orthodontics, Computer-aided design (CAD) and computer-
aided manufacturing (CAM) techniques have been reported for fabricating orthodontic devices,
such as removable appliances, customized lingual brackets and archwires, orthodontic models,
and occlusal splints to name a few (Nasef et al., 2014). Open and trusted connections with
7
orthodontic laboratories has facilitated quicker turnaround times and simplicity for digital
workflow (Kravitz et al., 2014).

One of the greatest orthodontic challenges is maintaining tooth position after debanding and thus
ensuring timely manufacturing of retainers is key to the success and longevity of orthodontic
treatment (Kravitz, 2010). Retainers fabricated from 3D printed models from digital scans have
been studied and shown to be perhaps higher quality when compared to those made from
conventional alginate impressions (Vasudavan et al., 2010; Tahir et al., 2018). Although current
advances in 3D printing have made it possible to print custom lingual fixed retainers (Doldo et
al., 2018; Nasef et al., 2014), the accuracy of current methods in directly 3D printing of
removable retainers (Nakano et al., 2019) remains controversial (Cole et al., 2019). Thus, most
in-office and external orthodontic labs 3D print models and fabricate the retainers from the
printed models (Claus et al., 2018). To go one step further, some orthodontic labs recently
introduced digital service of virtual bracket removal (VBR) in lieu of physical carving of
brackets from traditional plaster models. VBR can facilitate retainer delivery on the same day as
debond (Nelson, 2015). It is more convenient for patients since the retainers will be available
during the debonding procedure eliminating one additional visit to the office to install the
retainers. For the orthodontist, VBR prior to debonding appointment ensures more stable results
because there is no time that the patient is without retainers between debonding procedure and
retainer installation. Furthermore, minor tooth movements can be designed during the same
procedure of VBR, allowing for active retainers to be fabricated if needed. Although some
orthodontic laboratories offer VBR as a digital service, there is no scientific evidence regarding
the accuracy of VBR techniques for 3D printed models used for same-day retainer delivery. In a
8
previous preliminary study on typodonts (USC master’s thesis), Chamberlain demonstrated that
VBR is accurate and reproducible with in-house protocols established using either Meshmixer™
or Ortho Analyzer™ software (Chamberlain, 2019). Since both software were deemed
comparable in application and accuracy, the free-to-download Meshmixer™ was chosen for the
present study. The aim of the present clinical prospective study was to perform a 3D assessment
and compare the accuracy of the Meshmixer™ VBR protocol, established by Chamberlain, with
VBR performed by two separate orthodontic laboratories, ODL and NeoLab. The objective is to
determine if all three systems are accurate enough to be used in clinical practice for the
fabrication of modern orthodontic retainers using the digital workflow.

9
Materials and Methods
Sample
The University Park Institutional Review Board (UPIRB) approved this prospective clinical
study as a Human Subjects Research. Informed consent and assent were obtained from the legal
guardians and patients, respectively. This clinical study used the previously established
Meshmixer™ VBR protocol (Chamberlain, 2019) to compare the accuracy and reproducibility of
this protocol and two orthodontic laboratories (ODL and NeoLab). The sample consisted of 20
maxillary dentition intraoral scans of patients of the USC Advanced Orthodontic Clinic acquired
at either the completion of orthodontic treatment or before and after placement of all fixed
appliances. Each sample was de-identified by a third person and coded from “#1VBR to
#22VBR” to make the observers blinded during the measurements. The sample dropouts
included #1VBR and #14VBR, discarded due to scanning errors. The inclusion criteria were: 1)
patients who started or finished orthodontic treatment who had scans done on the same day as
bonding or debonding; and 2) full fixed labial appliances at least upper left first molar to upper
right first molar; and 3) each patient had at least one tooth per segment. Four groups were
compared (all subject participants were included in each group). Group 1 was the control group
that comprised of intra-oral scans without brackets (post-bracket removal scans/pre-bonding
scans). Group 2 was post-VBR scans from in-office (USC), using previously established protocol
with Meshmixer™ software. Group 3 was post-VBR scans from ODL Orthodontic Lab (Buffalo,
NY) and Group 4 was post-VBR scans from NeoLab (Andover, MA). Scans were taken twice in
the same time point: Before and after bonding or before and after debonding. The scans with and
without brackets were obtained on the same date for each patient using the TRIOS 3 Intraoral
Scanner (3Shape A/S, Copenhagen, Denmark) for conversion to STL format, which is an open
10
source, surface-based extension that can be used by most commercial and freeware software
applications (Gkantidis et al., 2015).

The TRIOS 3 scanner was calibrated before use and all scanning was performed by a trained
operator according to the manufacturer’s instructions (3Shape, 2015). The principal investigator
performed all of the scanning, acquiring 3D images with smooth and continuous motion.
Although the best images were obtained from a smooth scanning motion, occasional voids
occurred during initial acquisition of 3D images and rescanning of these areas was necessary.
Scans with large voids or obvious mismatching of 3D images were discarded and new 3D
images were acquired. Automatic post-processing was performed after scanning for detail
optimization, noise reduction, and void repair (3Shape, 2015).

Two groups of subjects were identified: Patients who would be beginning treatment with
bonding of upper first molar to first molar and patients finishing treatment and planned for
debond of upper first molar to first molar. The type of bracket bonded or debonded was not
important for the study, due to many bracket systems used in the USC Advanced Orthodontics
clinic. For group I, an intraoral scan taken prior to bonding using TRIOS 3 for conversion to
STL format served as the control group. For group II, an intraoral scan taken after debond and
clean-up using TRIOS 3 for conversion to STL format served as the control.

11
VBR Protocols
In-Office VBR
Although Meshmixer™ is not specifically designed for orthodontic applications, Chamberlain
previously established a protocol to virtually debond digital models using this software.
(Chamberlain, 2018). STL files from group I and group II were obtained and the maxillary scans
with brackets for each patient were taken during the same time point as the scans without
brackets (after debond or before bonding) for each subject participant. The previously
established protocol of Chamberlain for model preparation was performed. In some instances, it
was necessary to digitally remove scanning artifacts connected to brackets prior to VBR. Per the
USC VBR protocol, Surface Lasso selection mode allows surface faces to be selected without
“painting” (Autodesk, 2019). These selection boundaries can be refined using Smooth Boundary
tool. Table 1 and Figure 1 detail the previously established USC protocol for virtually debonding
using Meshmixer™

Out-of-office VBR
There are a few outside orthodontic laboratories that offer digital bracket removal as a service for
a fee. Two of these laboratories were selected based on their advertised capability to digitally
remove brackets: ODL Orthodontic Lab (Buffalo, NY) and NeoLab (Andover, MA). Both ODL
and NeoLab use Ortho Analyzer™ software for digital bracket removal. All STL files of the
bracketed maxillary arch for each subject participant were sent to both laboratories. Each subject
participant’s STL file was coded with a number as to not reveal the subject’s name (“VBR #2”
for example). ODL Orthodontic Lab uses EasyRx to submit prescriptions and NeoLab has their
own portal to submit prescriptions. Both labs performed digital bracket removal using Ortho
12
Analyzer™ and attached an STL file for each case after removal. ODL Orthodontic Lab
additionally fabricated an upper essix retainer for each case submitted following digital bracket
removal as well as a 3D printed resin model of the maxillary arch (Figure 2).

Superimposition
The STL files of all groups were imported into the 3-matic 3D modeling software (Materialise,
Leuven, Belgium) for 3D superimposition. The scans of group 2, group 3, and group 4 were
superimposed onto the group 1 models (control) using the surface-based technique (Figure 3).
The superimposition accuracy was evaluated by iterative closest point (ICP) algorithm and color-
coded maps to make sure the models were correctly superimposed. After that, the superimposed
STL files were exported and transferred to the VECTRA Analysis Module (VAM) for 3D
assessment of the virtual bracket removal procedures.

Measurements

All measurements were performed by two separate investigators using VAM to ensure inter-
examiner reliability and each examiner performed all of the measurements twice after a two-
week interval to ensure intra-examiner reliability. Four groups of maxillary arch STL files were
thus identified for the measurements: A control group of clinically debonded or pre-bonding,
VBR performed in-office by Meshmixer™ USC protocol, VBR performed by ODL Laboratory,
and VBR performed by NeoLab. For each sample (e.g. #2VBR) there was 1 model as the control
group and 3 models of VBR corresponding to the 3 methods of VBR. The 3 models that
underwent VBR in each sample were randomized and coded by Dr. Andre Weissheimer to
ensure blinding of the examiners performing the measurements. The 3 VBR models in each
sample were given the codes “U, X, or Y”, which each correspond to a respective lab, unknown
13
by the examiner performing the measurements. The coding system was revealed only after the
statistical analysis was complete. 3D Euclidean distances between surface points of the
superimposed control and debonded models were measured using VAM software. The
superimposed control and virtually debonded models were measured on the labial surface using
an ICP algorithm for comparative analysis of surface changes due to VBR for all 3 groups
(compared to the control group). The Color Surface by Distance tool (+/- 300um visualization
range) was used to illustrate surface changes via color mapping. For each tooth, the Paint Area
Selection tool was used to select the area where the bracket was virtually removed. A regional
color map of the selected area illustrated the linear surface changes due to VBR (Figures 4 and
5). VAM automatically calculated the minimum, maximum, root mean square (RMS), and mean
values with standard deviation of each selected area. Comparisons were made between the three
VBR techniques (Meshmixer™, ODL and Neolab), the tooth segments, and individual teeth.

Statistical Analysis

The linear surface changes due to VBR were measured for each technique (Meshmixer™, ODL,
and NeoLab) using 3D Euclidean distances between surface points in VAM software for
comparative analysis of linear surface changes due to VBR. The changes were also expressed via
regional color mapping. The RMS values, which best represented the overall magnitudes of
surface change irrespective of the direction of change, of the three techniques were compared.
Descriptive statistical analysis was performed with SPSS 20.0 for linear surface changes due to
VBR. Inter-operator (Cronbach’s alpha) and intra-operator (Cronbach’s alpha) reliability were
determined. The Shapiro-Wilk test was used to evaluate the normality of the data. Multiple linear
regression analysis with tooth/segment and lab as independent variables was first attempted.
Since lab was identified to contribute insignificantly to the total variance, one-way ANOVA was
14
used to detect the potential differences between three VBR protocols/labs, separate teeth and
tooth segments (incisors, canines/premolars, and first molars). Statistical differences between the
test groups were further analyzed with Scheffé’s post hoc test (⍺ = 0.05).

15
Results

Inter-operator and intra-operator reliability were determined to be high (>0.9). Since there were
no significant inter-operator and intra-operator differences in the VBR measurement reliability,
the RMS values from both operators and measurement trials were averaged by tooth for each
VBR group. R squared was determined to be 0.284 with both tooth segment and lab, but when
lab was disregarded, R squared was determined to be 0.27. Thus, the linear regression model
traced the lab as a minor factor in explaining the variance. Since lab was identified to contribute
insignificantly to the total variance, one-way ANOVA was used to detect the potential
differences between three VBR protocols/labs, separate teeth and tooth segments (incisors,
canines/premolars, and first molars). Statistical differences between the test groups were further
analyzed with Scheffe’s post hoc test (⍺ = 0.05).

One-way ANOVA used averaged RMS surface changes by tooth, central to first molar (1-6) and
significance was determined at p<0.05. RMS surface changes ranged from 0.1041mm to
0.3039mm, with first molars exhibiting the greatest amount of surface change and centrals
exhibiting the least amount of surface change from virtual bracket removal (Table II). The
pattern of distribution of the directional surface changes for each tooth is illustrated by Figure 6.
The second premolar and first molars showed the greatest distribution of directional surface
change, indicating the largest error in virtual bracket removal. Although RMS values were used
to conduct statistical analysis, and thus directionality of change (too much removal or too little
removal of bracket) cannot be inferred, color mapping indicated mostly blue hues for second
premolars and first molars, indicating insufficient bracket removal. Post hoc analysis with
Scheffé’s test (Table III) showed a pairwise statistically significant difference (p<0.05) in
16
averaged RMS values between the second premolar and all other teeth in the arch as well as the
first molar and all other teeth in the arch. There was no statistically significant difference
between all other pairs with the centrals, laterals, canines, and first premolars.

The one-way ANOVA of the tooth segments (incisors, canine/premolars, and first molar) using
averaged RMS surface changes and a significance determined at p< 0.05 showed RMS surface
changes ranging from 0.1183mm to 0.3308mm, with the first molars exhibiting the greatest
amount of surface change and the incisors exhibiting the least amount of surface change from
virtual bracket removal. The pattern of distribution of the directional surface changes for each
segment (incisors, canine/premolars, and first molars) is illustrated in Figure 7. The first molar
segment showed the greatest distribution of directional surface change, indicating the largest
error in virtual bracket removal. Post hoc analysis with Scheffé’s test (Table V) showed a
pairwise statistically significant difference (p<0.05) in averaged RMS values between the first
molar segment and the incisors as well as the first molar segment and the canine/premolar
segment. There was also a statistically significant difference between the incisors and the
canine/premolar segment.

In evaluating the differences between the in-house method and two labs, a one-way ANOVA
was performed using averaged RMS surface changes by lab (Meshmixer™ , ODL, and NeoLab).
Significance was determined at p<0.05. RMS surface changes ranged from 0.1512mm to
0.1860mm with NeoLab exhibiting the least amount of surface changes and ODL exhibiting the
greatest amount of surface change following virtual bracket removal (Table VI). The pattern of
distribution of the directional surface change illustrated by a box plot (Figure 8), indicated the
17
largest error in virtual bracket removal. The in-house method and the two labs showed a similar
distribution of surface change which indicates a similar error in virtual bracket removal. Post hoc
analysis with Scheffé’s test (Table VII) showed a pairwise statistically significant difference
(p<0.05) in averaged RMS values between ODL and NeoLab. No statistically significant
difference was found between the Meshmixer™ and ODL as well as Meshmixer™ and NeoLab.

In addition to quantitative analysis, color mapping illustrated surface changes due to VBR using
Meshmixer™, ODL, and NeoLab (Figure 4 and 5). Color map visualization showed that
negative surface changes (red hues) were more prevalent in the incisors and canine segments
which positive surface changes (blue hues) were more prevalent in the premolar/first molar
segments. The direction of surface change was averaged for each sample tooth across all four
time points. A negative surface value, colored red by the color mapping, indicated that too much
bracket was digitally removed as compared the control (unintentional tooth surface removal). A
positive surface value, colored blue by the color mapping, indicated that some bracket remained
on the tooth following digital bracket removal (inadequate bracket removal). The net negative
and positive values were added up and percentage of negative and positive surface changes
calculated for each lab (Table VIII). Of the total sample of 223 teeth, NeoLab exhibited the
greatest percentage of unintentional tooth surface removal with 29.7%. ODL exhibited 13.7% of
intentional tooth surface removal and Meshmixer™ showed the least unintentional tooth surface
removal with 8.9%.

18
Discussion

The purpose of this study was to compare the accuracy of the in-house Meshmixer™ USC VBR
protocol and VBR techniques of two outside laboratories (ODL and NeoLab). In assessing the
accuracy of 3D imaging, the process of image registration is used, which is the process of
overlaying and integrating two or more 3D images (Brown, 1992; Lin et al., 2013; Zitová and
Flusser, 2003).

Although several commercially available intraoral scanners generate reliable digital models (Anh
et al., 2016; Flügge et al., 2013; Gan et al., 2016; Lim et al. 2018; Sousa et al., 2012; Wiranto et
al., 2013), TRIOS 3 was preferred in this study because it is less likely to be influenced by
scanning technique (Anh et al., 2016; Ender and Mehl, 2013) or the length of clinical career
(Lim et al., 2018). TRIOS 3 has also demonstrated reliable scanning accuracy of dentitions with
fixed labial appliances (Claus et al., 2018; Jung et al., 2016; Park et al., 2016), which was critical
for this study.

Studies have shown that image registration using an iterative closest point (ICP) algorithm of
surface-based registration (Besl and McKay, 1992) is more reliable than landmark-based
registration because surface-based registration is based on thousands of surface points rather than
a few manually selected landmarks (Ghoneima et al., 2017; Gkantidis et al., 2015; Grauer et al.,
2009). ICP is a registration technique used to register two corresponding point sets by calculating
the minimum distance for the closest points iteratively (Lin et al., 2013; Besl and McKay, 2013).
Thus, surface-based image registration techniques should be used when evaluating the accuracy
of digital records. Accuracy of digital scans is evaluated best by superimposing the scans using
19
surface points calculated by 3D Euclidean distances (Ender et al., 2016). 3D surface data of
digital models can be superimposed reliably using surface-based registration using an ICP
algorithm (Brown, 1992; Ghoneima et al., 2017; Lin et al., 2013). The ability to make
meaningful conclusions about error due to VBR relied on scanning accuracy. Therefore, surface-
based superimposition of the control and bonded models using an iterative closest point (ICP)
algorithm (Besl and McKay, 1992) was performed to verify accuracy.

Using these superimposed surfaces, accuracy can be evaluated by measuring linear distances
between the superimposed surfaces (Ender and Mehl, 2013). In the current study, accuracy was
assessed 3D surface-based superimposition of the control and virtually debonded models and
using VAM’s Color Surface by Distance tool (±300 µm visualization range) to measure and
display linear surface changes between the models. The Paint Area Selection tool, which is
designed for identifying regions of dimensional differences (Scientific, 2018; Claus et al., 2019),
was used to generate regional color maps on the labial surfaces where the brackets were virtually
removed for quantitative analysis of the minimum values, maximum values, RMS values, and
standard deviations. Unlike the previous laboratory protocol study (Chamberlain, 2019), red hues
indicated negative values (zones of unintentional tooth surface removal) and blue hues indicated
positive values (zones of inadequate bracket removal) in the current study. Averaged RMS
values, and not the average of mean values, was used in our study because it represents the
overall magnitude of surface changes, whereas the greatest value of minimum or maximum for
each tooth detailed the net direction of change. Comparisons of averaged RMS surface changes
were made between individual teeth, tooth segments, and the three labs. The greatest value
between the minimum and maximum values for each tooth was taken to calculate the percentage
20
of inadequate bracket removal (positive value) or unintentional tooth surface removal (negative
value) for each lab.

For the analysis of individual teeth in the sample, the first molars and second premolars exhibited
a statistically significant greater amount of surface change (0.3308mm for first molars and
0.1954mm for second premolars) compared to the first premolars, canines, laterals, and centrals.
This finding indicates that VBR is least accurate for second premolars and first molars. Scheffé’s
post hoc analysis showed a statistically significant difference in RMS surface changes between
second premolars and all other teeth as well as first molars and all other teeth. Surface changes
were greater for second premolars and first molars and color mapping showed a prevalence of
blue hues on the labial surfaces of the first premolars and first molar. Positive surface changes
(blue hues) indicated inadequate removal of bracket during VBR, whereas negative surface
changes (red hues) indicated unintentional tooth surface removal. Greatest error detected in VBR
of the first molar and second premolar could be due to the proximity of the brackets to the
gingival margin, which may interfere with the algorithm used by the software when computing
the selected area and removing the bracket. Canines and incisors have brackets positioned in the
middle of the crown and away from the gingival margins, which may explain the better accuracy.

When analyzing the tooth segments, all three segments showed a statistically significant
difference when compared to each other. The first molar segment displayed the highest RMS
value of 0.3308mm, the canine/premolar segment displayed the second highest RMS value of
0.1441mm, and the incisors showed the smallest RMS value of 0.1183mm. This finding
indicates that VBR is least accurate in the first molar region. Scheffé’s post hoc analysis showed
21
that the first molar showing the highest statistically significant difference when compared to
either the incisors or the canine/premolar segments, but the canine/premolar segment also
displayed a statistically significant difference when compared to the incisors segment.
Furthermore, color mapping showed a higher prevalence of blue hues (positive surface change)
in the posterior segments, implying that there is a tendency to have inadequate bracket removal
in this region as compared to the incisors. This greater error in the posterior segments could
again be attributed to the interference with the algorithm in bracket removal when the bracket is
closer to the gingival margin, as was the case with the second premolar bracket and the first
molar bracket. The accuracy of VBR increases from posterior to anterior and it is influenced by
the bracket proximity to the gingiva with posterior brackets usually closer to the gingival margin
(first molar tubes/hooks and second premolar bracket) in comparison to the brackets bonded in
the anterior teeth. In consistency with the individual tooth measurements, the greatest error
appeared in the first molar segment. The second premolar likely increased the average of the
premolar and canine segment, making it exhibit the next greatest error. Although studies have
shown that intraoral scanning with TRIOS 3 is least accurate in the molar region (Anh et al.,
2016; Ender et al., 2016; Flügge et al., 2013; Rudolph et al., 2007), scanning error is less than
0.10mm at the distal tooth (Ender et al., 2016). Since scanning accuracy was verified to be within
0.10mm, surface changes seen at all three segments was likely due to virtual debonding error and
not due to scanning inaccuracy. One explanation for this is that the software may not be able to
navigate strong changes in surface curvature (Chamberlain, 2019; Rudolph et al., 2007).
Although RMS values for all three tooth segments were greater than 0.1mm, they were all within
the reported accuracy for orthodontic casts, which is 0.3-0.5mm (Cole et al., 2019).

22
Finally, analysis between the three labs (USC in-house Meshmixer™, ODL, and Neolab) showed
that ODL had the highest RMS value of surface change with 0.1860mm, Meshmixer™ second
highest with 0.1684mm, and NeoLab had the lowest surface change with 0.1512mm. Despite the
post hoc analysis displaying a statistically significant difference between ODL and NeoLab, it is
likely that this finding is not clinically significant. No significant difference was detected in
surface changes from VBR between Meshmixer™ and the two other labs. Thus, there is likely
very little clinical significance in the accuracy of VBR between all three labs. Additionally, it
was determined through color mapping and detailing the directional greatest amount of surface
change after VBR of each model, whether the error tendency was toward unintentional tooth
surface removal or inadequate bracket removal. For all three labs, there was a higher percentage
of inadequate bracket removal (91.1% for Meshmixer™, 86.3% for ODL, and 70.3% for
NeoLab). NeoLab displayed the highest percentage of unintentional tooth surface removal with
29.7%. Unintentional tooth surface removal may be clinically significant if it is great enough
because vacuum-formed thermoplastic retainers may not adapt well to areas where tooth surface
was unintentionally removed. Although average RMS surface changes were less than 0.5mm,
which is within the guideline for accuracy for orthodontic models (Cole et al., 2019), it is unclear
if this only refers to positive change as opposed to negative change. Since optimal adaptation to
the dentition is critical for retention (Rudolph et al., 2017), additional research should be done to
assess the clinical significance of unintentional removal of tooth structure on the fit of the
retainers designed from VBR models. One limitation of this study is that both out of office labs
used Ortho Analyzer
TM
as opposed to Meshmixer
TM
. Although the different software used for
comparison in this study is a limitation, the previous study on typodonts illustrated that VBR
using either Ortho Analyzer
TM
or Meshmixer
TM
is clinically acceptable (Chamberlain, 2019).
23

Recent studies have focused on the application of digital models on CAD/CAM appliance
fabrication (Claus et al., 2018; Jheon et al., 2017; Nasef et al., 2014; Park et al., 2016). Although
the clinical acceptability of appliances fabricated from digital and conventional models is
comparable (Vasudaven et al., 2010), optimal adaptation to the dentition is dependent on the
accuracy of the working models. There is a risk for inaccuracy with digital models because
multiple, consecutively recorded 3D images must be assembled by the intraoral scanner (Claus et
al., 2018). However, it has been demonstrated that the accuracy of conventional intraoral
scanners, including TRIOS, is sufficient for numerous orthodontic applications (Claus et al.,
2018; Ender and Mehl, 2013; Flügge et al., 2013; Gan et al., 2016; Jung et al., 2016; Lim et al.,
2018). In the recent laboratory study done by Chamberlain in 2019, it was concluded that VBR
using Ortho Analyzer™ and Meshmixer™ can be utilized in computer-aided design and
manufacture of retainers. Chamberlain showed that by superimposition of control and bonded
digital models prior to VBR, less than 0.10mm of error was due to intraoral scanning. Any
surface changes greater than 0.10mm between the control and virtually debonded models were
attributable to VBR and considered virtual debonding error but it was determined that it was
unlikely that errors less than 0.10mm would create a clinical significance on the adaptation of a
retainer fabricated from a digital working model (Chamberlain, 2019). Although it has been
stated that an error greater than 0.1mm could have clinical significance on the adaptation of
orthodontic appliances (Wiranto et al., 2013), a recent study that evaluated fit of 3D-printed
retainers concluded that error of up to 0.5mm is clinically acceptable for fit of orthodontic
retainers (Cole et al., 2019).

24
The current study applied the concepts and findings of the previous study by Chamberlain to a
clinical study comparing the clinical accuracy between in-house VBR using Meshmixer™ with
ODL and Neolab, who both use OrthoAnalyzer™ to perform VBR. In comparison of tooth
segments, the findings of this study contradict the previous study done by Chamberlain. In
contrast to the previous preliminary study, which used 3D printed typodont models, the current
study applied the same concepts to a clinical study on patients. Although the current study
confirmed that VBR has a tendency to be significantly less accurate in the posterior segment, the
current study determined that there is a tendency toward average positive change (inadequate
bracket removal) across all groups as opposed to the average negative change (unintentional
bracket removal) that was found by the previous study. This difference could be due to inter-
operator error. The current study confirmed that despite a small statistically significant difference
between ODL and NeoLab, there is likely no clinical significance in accuracy between the three
labs. Thus, VBR using the in-house USC Meshmixer™ method as well as sending out to either
ODL or NeoLab for VBR can be used in computer-aided design and manufacture of retainers.

High inter-operator reliability, similar to the previous study on typodonts (Chamberlain, 2019)
suggests that VBR can be delegated to trained auxiliaries, such as in-house lab technicians.
Fabrication of retainers from virtually debonded models can help facilitate same-day delivery
and therefore eliminate undesired tooth movement that can occur during the time period between
debond and retainer delivery by conventional means.

Table IX outlines the features of in-office VBR and the two laboratories, including the cost
analysis. In-office VBR using Meshmixer™ has the advantage of being free to download and a
quicker turnaround time if one has the ability to print in-office. ODL has a faster turnaround time
25
than NeoLab and is comparable in price. Both ODL and NeoLab use Ortho Analyzer™ to
perform VBR. As detailed in the study by Chamberlain, in-office VBR can also be performed
using Ortho Analyzer™ and this software is commercially available. Due to the limited sample
size and use of only labially bonded metal brackets for VBR in this study, future studies should
focus on VBR of other bracket materials such as clear ceramic brackets, lingual brackets, and
aligner attachments. Additionally, future studies should use the findings and techniques outlined
by this study and the previous study by Chamberlain to investigate the fit of retainers made off of
VBR models versus those made from conventionally debonded models.

26
Conclusions

In this prospective clinical study, the virtual bracket removal techniques were comparatively
accurate using the USC Meshmixer™ VBR protocol or by the two outside orthodontic labs,
ODL and Neolab. VBR accuracy increases from posterior teeth to anterior teeth. All three
methods demonstrated acceptable VBR accuracy for computer-aided design and manufacture of
retainers.

27
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31
List of Tables

Table I. Meshmixer
TM
Bracket Removal Workflow
1. The bonded arch model was imported in STL format
2. Artifacts connected to a bracket were selected using Brush selection mode (Unwrap-mode,
size 20) together with zoom and 3D rotational controls
a. Edit menu > Discard operation (Hotkey “X”)
3. Voids resulting from deleted artifacts were repaired
a. Analysis menu > Inspector tool (Hotkey “I”)
b. Void repair by left-clicking blue sphere(s)
4. Zoom and 3D rotational controls were used to manipulate the digital model such that only
UR7, UR6, and UR5 were in view
5. Starting at the distogingival aspect of UR6, each bracket was slowly circumscribed using
Surface Lasso selection mode together with 3D rotational controls
a. Modify menu > Smooth Boundary tool (Hotkey “B”) > Accept (Hotkey “Enter”)
b. Edit menu > Erase & Fill operation (Hotkey “F”) > Accept (Hotkey “Enter”)
6. Brackets were virtually debonded sequentially from UR6 to UL6
7. Debonded models were exported in STL format
UR6, upper right first molar; UL6, upper left first molar

32
Table II: Summary of averaged RMS (mm) of each maxillary tooth following virtual bracket
removal
Tooth RMS SD
1 (Central) 0.1041 0.05731
2 (Lateral) 0.1331 0.06657
3 (Canine) 0.1189 0.06540
4 (First Premolar) 0.1154 0.06495
5 (Second Premolar) 0.1954 0.11228
6 (First Molar) 0.3308 0.16569
RMS, root mean square; SD, standard deviation

33
Table III. Scheffé’s post hoc analysis of RMS values (mm) by tooth
Pair Mean Difference Std. Error p-Value
1 v. 2 -0.02896 0.01283 0.405
1 v. 3 -0.01478 0.01274 0.930
1 v. 4 -0.01129 0.01368 0.984
1 v. 5 -0.09125 0.01301 0.000***
1 v. 6 -0.22662 0.01266 0.000***
2 v. 3 0.01418 0.01291 0.944
2 v. 4 0.01767 0.01383 0.897
2 v. 5 -0.06229 0.01317 0.001***
2 v. 6 -0.19766 0.01283 0.000***
3 v. 4 0.00349 0.01375 1.000
3 v. 5 -0.07647 0.01309 0.000***
3 v. 6 -0.21184 0.01274 0.000***
4 v. 5 -0.07966 0.01400 0.000***
4 v. 6 -0.21533 0.01368 0.000***
5 v. 6 -0.13537 0.01301 0.000***
*- p ≤0.05. The mean difference is significant at the 0.05 level
**- p ≤0.01
***- p≤0.001

34
Table IV: Summary of averaged RMS (mm) of each tooth segment following virtual bracket
removal
Segment RMS SD
Incisors 0.1183 0.06354
Canine/Premolar 0.1441 0.09192
First Molar 0.3308 0.16569
RMS, root mean square; SD, standard deviation

35
Table V. Scheffé’s post hoc analysis of RMS values (mm) by tooth segment
Pair Mean Difference Std Error p-Value
Incisors v.
Canine/Premolar
-0.02589 0.00876 0.013*
Incisor v. First molar -0.21251 0.01140 0.000***
Canine/Premolar v.
first molar
-0.18662 0.01089 0.000***
*- p ≤0.05. The mean difference is significant at the 0.05 level
**- p ≤0.01
***- p≤0.001

36
Table VI: Summary of averages RMS surface changes of the models debonded with
Meshmixer
TM
, ODL, and Neolab
VBR Lab RMS SD
Meshmixer
TM
0.1684 0.13658
ODL 0.1860 0.12913
Neolab 0.1512 0.11269
RMS, root mean square; SD, standard deviation

37
Table VII: Scheffé’s post hoc analysis of RMS values (mm) by lab.
Pair Mean Difference Std. Error p-Value
Meshmixer
TM
v. ODL -0.01759 0.01198 0.341
Meshmixer
TM
v. Neolab 0.01723 0.01198 0.356
ODL v. Neolab -0.03483 0.01198 0.015*
*- p ≤0.05. The mean difference is significant at the 0.05 level
**- p ≤0.01
***- p≤0.001

38
Table VIII: Percentage negative and positive surface change following digital bracket
removal per lab
Meshmixer
TM
ODL NeoLab
Negative Surface
Change (% of 223)
8.9% 13.7% 29.7%
Positive Surface
Change (% of 223)
91.1% 86.3% 70.3%

39
Table IX: Multifactorial comparison between Meshmixer™, ODL, and NeoLab
Factor Meshmixer™ ODL NeoLab
VBR Software Used Meshmixer™ Ortho Analyzer™ Ortho Analyzer™
Cost Free + cost to print $48.50 (one arch
VBR + Printed
model + essix)
$45 single arch
package or $90 full
arch package
(includes VBR, 3D
models, and essix)
Turnaround Time ~10mins for VBR +
time to send out to
print or perform in
office
~7-10 days ~14 days
Practical Application VBR only + print in
office for retainer or
send out to print
Offers VBR + 3D
printed model +
essix retainer as a
package
Offers VBR only or
VBR + 3D printed
model + essix retainer

40
List of Figures

Figure 1

41
Figure 2

A B
42
Figure 3

43
Figure 4

44
Figure 5

45
Figure 6

46
Figure 7

47
Figure 8

48
Figure Legends

Figure 1. In-office Meshmixer™ Workflow. A. The tooth with the bracket was visualized; B.
The bracket was selected using Surface Lasso mode; C. Smooth Boundary tool (Hotkey “B”)
was applied; D. The irregular selection boundary around the bracket was refined; E. Virtual
removal of the bracket using Erase & Fill operation (Hotkey “F”) set to property panel defaults;
F. Visualization of tooth following digital bracket removal with Meshmixer™

Figure 2. ODL Models. A. Occlusal view of 3D printed model and thermoform retainer from
ODL, included in package with VBR for single arch. B. Frontal view of 3D printed model and
thermoform retainer from ODL.

Figure 3: Superimposition onto Control. Superimposition of Group 2 (pink) onto control Group
1 (grey) using surface-based technique.

Figure 4: Whole Arch VAM Workflow. Illustration of VAM method of whole arch used to
measure linear surface change. A. Arch prior to VBR with brackets; B. Arch following VBR
using each lab; C. Illustration of surface-based superimposition (whole arch) of a virtually
debonded model and its corresponding control model using VAM; D. Selection of area where
bracket was virtually removed using Paint Area Selection tool. E. Regional color map of linear
surface changes of selected area due to VBR. Color map: Red hues indicate negative value
where unintentional tooth surface removal occurred. Blue hues indicate positive values where
inadequate bracket removal occurred.

Figure 5: VAM Workflow By Tooth. Illustration of VAM method of each tooth in the maxillary
arch (1-6). A. Tooth prior to VBR with bracket; B. Tooth directly following VBR using each lab;
49
C. Tooth directly after bracket removal superimposed on control tooth; D. Selection of area
where bracket was virtually removed using Paint Area Selection tool; E. Regional color map of
linear surface changes of selected area of each tooth due to VBR. Color map: Red hues indicate
negative value where unintentional tooth surface removal occurred. Blue hues indicate positive
values where inadequate bracket removal occurred.

Figure 6. Boxplot illustrating RMS surface changes distributions by tooth. Tooth 1 represents
the central incisor, tooth 2 represents the lateral incisor, tooth 3 represents the canine, tooth 4
represents the first premolar, tooth 5 represents the second premolar, and tooth 6 represents the
first molar. The distribution of RMS surface change values was greatest for the tooth 5 and 6
with tooth 6 showing the greatest distribution and highest median.

Figure 7. Boxplot illustrating RMS surface change values distribution by tooth segment.
Segment 0 includes the incisors, segment 1 includes the canine/premolars, and segment 2
includes the first molars. Segment 2 displayed the greatest distribution of RMS values for surface
changes and the greatest median value.

Figure 8. Boxplot illustrating RMS values distribution by lab. Lab 0 refers to VBR done by the
USC Meshmixer™ protocol, lab 1 refers to VBR done by ODL, and lab 2 refers to VBR done by
NeoLab. There is a similar distribution of RMS values of surface changes among all three labs.

50
Appendix
Sample # Min Max Max or Min?
Greatest
Value RMS Mean SD
2U- UR6 0.0348808 0.554276 Max 0.554276 0.250948 0.20357 0.146745
2U-UR5 -0.0692039 0.0772438 Max 0.0772438 0.0395426 0.0148486 0.0366488
2U-UR4 -0.114705 0.0498125 Min -0.114705 0.0396589 -0.00501506 0.0393406
2U-UR3 -0.00584973 0.160288 Max 0.160288 0.0701586 0.0513494 0.0478065
2U-UR2 -0.0178161 0.202225 Max 0.202225 0.0916872 0.0684483 0.061003
2U-UR1 -0.0107638 0.135761 Max 0.135761 0.0713689 0.0534375 0.047307
2U-UL1 -0.00693819 0.0848672 Max 0.0848672 0.0378507 0.0252689 0.0281809
2U-UL2 -0.0302773 0.114751 Max 0.114751 0.0425005 0.0322172 0.0277191
2U-UL3 -0.00266436 0.206808 Max 0.206808 0.0821509 0.0663282 0.0484701
2U-UL4 -0.102166 0.0715932 Min -0.102166 0.0468639 0.016125 0.0440024
2U-UL5 -0.0508418 0.0738934 Max 0.0738934 0.0420515 0.0202241 0.0368688
2U-UL6 -0.00176052 0.392046 Max 0.392046 0.164499 0.128483 0.102724
2X-UR6 -0.235561 0.409314 Max 0.409314 0.237453 0.193643 0.137427
2X-UR5 -0.0524976 0.0510725 Min -0.0524976 0.0249482 0.0108152 0.022482
2X-UR4 0.0625981 0.485619 Max 0.485619 0.265204 0.24152 0.10955
2X-UR3 0.0143847 0.405879 Max 0.405879 0.243275 0.220816 0.102095
2X-UR2 -0.174495 0.2485 Max 0.2485 0.127237 0.0794601 0.0993748
2X-UR1 -0.0426568 0.382604 Max 0.382604 0.193121 0.150399 0.121144
2X-UL1 -0.0257026 0.224556 Max 0.224556 0.102252 0.0806257 0.0628884
2X-UL2 0.00319656 0.278821 Max 0.278821 0.11367 0.0883571 0.0715111
2X-UL3 0.0367264 0.344912 Max 0.344912 0.192324 0.175152 0.0794373
2X-UL4 -0.15033 0.0987789 Min -0.15033 0.0538747 0.00710884 0.0534036
2X-UL5 -0.0299826 0.166734 Max 0.166734 0.0626216 0.0464447 0.0420042
2X-UL6 -0.0724906 0.437204 Max 0.437204 0.213405 0.177631 0.118275
2Y-UR6 -0.0724493 0.396431 Max 0.396431 0.217073 0.193464 0.0984503
2Y-UR5 0.0241646 0.178169 Max 0.178169 0.10917 0.102883 0.0365128
2Y-UR4 -0.086915 0.0476177 Min -0.086915 0.0292855 0.00104072 0.029267
2Y-UR3 -0.0173489 0.214821 Max 0.214821 0.108087 0.0943704 0.0526967
2Y-UR2 -0.00673387 0.251827 Max 0.251827 0.161168 0.146951 0.0661849
2Y-UR1 -0.0270168 0.235709 Max 0.235709 0.126984 0.100649 0.0774255
2Y-UL1 -0.0266771 0.111708 Max 0.111708 0.0393575 0.0257465 0.029768
2Y-UL2 0.0480732 0.329768 Max 0.329768 0.214702 0.205234 0.0630555
2Y-UL3 -0.00635049 0.264518 Max 0.264518 0.1545 0.137304 0.0708367
2Y-UL4 -0.00901417 0.164821 Max 0.164821 0.087972 0.0804956 0.0354899
2Y-UL5 -0.0549214 0.119027 Max 0.119027 0.068044 0.0519325 0.043966
2Y-UL6 -0.00657501 0.319977 Max 0.319977 0.164036 0.144232 0.0781348
3U-UR6 0.165848 0.476547 Max 0.476547 0.302499 0.289158 0.0888445
3U-UR5 -0.0496891 0.340367 Max 0.340367 0.174066 0.133164 0.112099
3U-UR4 - - - - -
3U-UR3 -0.0188309 0.0792471 Max 0.0792471 0.0384642 0.0285831 0.0257391
3U-UR2 -0.0164412 0.155467 Max 0.155467 0.096776 0.0838705 0.0482838
3U-UR1 -0.0075403 0.182145 Max 0.182145 0.0925351 0.0742996 0.0551572
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20X-UR4 -0.0994611 0.468642 Max 0.468642 0.221727 0.160706 0.152764
20X-UR3 -0.0447512 0.423433 Max 0.423433 0.177965 0.145859 0.101963
20X-UR2 -0.0538797 0.230947 Max 0.230947 0.121579 0.105792 0.0599119
20X-UR1 -0.0267897 0.256748 Max 0.256748 0.128572 0.106226 0.072435
20X-UL1 -0.0387854 0.249244 Max 0.249244 0.102598 0.0762065 0.0686943
20X-UL2 -0.0631228 0.321305 Max 0.321305 0.153551 0.110881 0.106223
20X-UL3 -0.159072 0.348354 Max 0.348354 0.0919054 0.0162177 0.0904632
20X-UL4 -0.137039 0.197947 Max 0.197947 0.0882763 0.0303909 0.08288
20X-UL5 - -
20X-UL6 -0.094253 1.04451 Max 1.04451 0.424037 0.303318 0.296319
20Y-UR6 -0.141901 0.512187 Max 0.512187 0.206268 0.130506 0.159733
20Y-UR5 - -
20Y-UR4 -0.175692 0.283129 Max 0.283129 0.0904859 -0.00917086 0.09002
20Y-UR3 -0.0976362 0.390696 Max 0.390696 0.124963 0.0918866 0.0846912
20Y-UR2 -0.0017687 0.336439 Max 0.336439 0.173289 0.146321 0.0928392
20Y-UR1 -0.0365913 0.182196 Max 0.182196 0.0750009 0.0575159 0.0481357
20Y-UL1 -0.00931843 0.260585 Max 0.260585 0.0887582 0.0740611 0.048918
20Y-UL2 -0.0531424 0.324599 Max 0.324599 0.143834 0.106385 0.0968013
20Y-UL3 -0.219614 0.228945 Max 0.228945 0.0965501 -0.0435171 0.0861869
20Y-UL4 -0.107582 0.185734 Max 0.185734 0.0757028 0.0216826 0.0725313
20Y-UL5 - -
20Y-UL6 -0.116076 0.876041 Max 0.876041 0.335282 0.205354 0.265035
21U-UR6 0.0602852 0.768089 Max 0.768089 0.37564 0.329889 0.179662
21U-UR5 -0.013959 0.699493 Max 0.699493 0.325714 0.239553 0.22069
21U-UR4 -0.0542185 0.430615 Max 0.430615 0.186622 0.13103 0.132886
21U-UR3 -0.0580609 0.464752 Max 0.464752 0.155347 0.0752971 0.135878
21U-UR2 -0.0462406 0.320715 Max 0.320715 0.171574 0.146001 0.090117
21U-UR1 -0.0277049 0.351682 Max 0.351682 0.185241 0.148449 0.110803
21U-UL1 -0.0458028 0.320893 Max 0.320893 0.148711 0.103596 0.106691
21U-UL2 0.00263627 0.298233 Max 0.298233 0.163015 0.141933 0.0801805
21U-UL3 0.0377355 0.190175 Max 0.190175 0.106181 0.0998714 0.0360578
21U-UL4 -0.0736282 0.340297 Max 0.340297 0.172827 0.116588 0.127579
21U-UL5 0.031804 0.826528 Max 0.826528 0.32188 0.269305 0.1763
21U-UL6 0.0540213 0.648933 Max 0.648933 0.351948 0.316626 0.153673
21X-UR6 -0.0703431 0.771119 Max 0.771119 0.424663 0.366622 0.214305
110
21X-UR5 -0.0976114 0.570059 Max 0.570059 0.244603 0.195755 0.146665
21X-UR4 -0.0697232 0.489916 Max 0.489916 0.239451 0.207367 0.119733
21X-UR3 -0.0774701 0.449943 Max 0.449943 0.204206 0.160273 0.126542
21X-UR2 -0.0648144 0.385171 Max 0.385171 0.248015 0.231995 0.0876889
21X-UR1 -0.078208 0.82451 Max 0.82451 0.361966 0.27493 0.235442
21X-UL1 -0.0764011 0.485124 Max 0.485124 0.204112 0.155284 0.132471
21X-UL2 -0.0489605 0.313867 Max 0.313867 0.138203 0.113202 0.0792802
21X-UL3 -0.0831386 0.261646 Max 0.261646 0.119867 0.0843261 0.08519
21X-UL4 -0.177707 0.243891 Max 0.243891 0.104512 -0.00195646 0.104494
21X-UL5 -0.0654837 0.536873 Max 0.536873 0.26651 0.223158 0.145697
21X-UL6 -0.0508086 0.700431 Max 0.700431 0.387934 0.342439 0.182285
21Y-UR6 -0.0471242 0.880868 Max 0.880868 0.45719 0.384079 0.248005
21Y-UR5 -0.0894282 0.700121 Max 0.700121 0.326348 0.257605 0.200356
21Y-UR4 -0.1105 0.468611 Max 0.468611 0.188753 0.131206 0.135693
21Y-UR3 -0.095071 0.432642 Max 0.432642 0.138304 0.0785593 0.113826
21Y-UR2 -0.0648994 0.312558 Max 0.312558 0.15177 0.121846 0.0904862
21Y-UR1 -0.0868349 0.29133 Max 0.29133 0.155331 0.108627 0.111031
21Y-UL1 -0.119576 0.213264 Max 0.213264 0.0720299 0.0135364 0.0707465
21Y-UL2 -0.0484414 0.405518 Max 0.405518 0.146658 0.111113 0.0957204
21Y-UL3 -0.000625156 0.402652 Max 0.402652 0.232835 0.201503 0.116657
21Y-UL4 0.00436998 0.417767 Max 0.417767 0.212853 0.187426 0.100886
21Y-UL5 -0.0362297 0.810682 Max 0.810682 0.40989 0.356161 0.202878
21Y-UL6 -0.0352762 0.785245 Max 0.785245 0.440316 0.375686 0.229648
22U-UR6 -0.0224818 0.448463 Max 0.448463 0.168531 0.132036 0.104734
22U-UR5 - -
22U-UR4 -0.0464595 0.264126 Max 0.264126 0.0963873 0.0584567 0.0766377
22U-UR3 -0.0417836 0.199319 Max 0.199319 0.0876369 0.059413 0.0644229
22U-UR2 -0.0667326 0.318545 Max 0.318545 0.131138 0.0740763 0.108212
22U-UR1 -0.0356375 0.134565 Max 0.134565 0.0547285 0.0262239 0.0480366
22U-UL1 -0.00618454 0.118012 Max 0.118012 0.0494124 0.039227 0.0300472
22U-UL2 -0.049857 0.223673 Max 0.223673 0.0701841 0.0417112 0.0564446
22U-UL3 -0.0388789 0.149343 Max 0.149343 0.0394443 0.0222211 0.0325896
22U-UL4 -0.0434573 0.10421 Max 0.10421 0.0403524 0.0303491 0.0265943
22U-UL5 - -
22U-UL6 -0.035666 0.963802 Max 0.963802 0.39465 0.27894 0.279179
22X-UR6 -0.0915595 0.756589 Max 0.756589 0.30334 0.230804 0.196838
22X-UR5 - -
22X-UR4 -0.0880732 0.284084 Max 0.284084 0.0916519 0.0384942 0.0831761
22X-UR3 -0.112054 0.176208 Max 0.176208 0.0667759 0.0253957 0.0617582
22X-UR2 -0.116477 0.222828 Max 0.222828 0.0992794 0.0329007 0.0936694
22X-UR1 -0.0909574 0.161875 Max 0.161875 0.0724073 0.016186 0.0705749
22X-UL1 -0.0466882 0.178682 Max 0.178682 0.070282 0.0400263 0.0577707
22X-UL2 -0.191202 0.194448 Max 0.194448 0.0861922 0.0333616 0.0794739
22X-UL3 -0.0971012 0.0363818 Min -0.0971012 0.0376826 -0.0234236 0.0295181
22X-UL4 -0.131447 0.121239 Min -0.131447 0.04927 0.0123527 0.0476963
22X-UL5 - -
22X-UL6 -0.00836376 0.826345 Max 0.826345 0.376688 0.311843 0.2113
111
22Y-UR6 -0.0968625 0.413309 Max 0.413309 0.123567 0.0771429 0.096529
22Y-UR5 - -
22Y-UR4 -0.0394109 0.377826 Max 0.377826 0.117187 0.0708458 0.0933474
22Y-UR3 -0.146959 0.0982848 Min -0.146959 0.052108 -0.014934 0.0499221
22Y-UR2 -0.0961696 0.121554 Max 0.121554 0.0485898 -0.0014517 0.0485681
22Y-UR1 -0.0845741 0.0666182 Min -0.0845741 0.0387088 -0.0199681 0.0331609
22Y-UL1 -0.0733073 0.0632894 Min -0.0733073 0.0278087 -0.00766478 0.0267316
22Y-UL2 -0.158404 0.0669495 Min -0.158404 0.0512635 -0.010329 0.0502121
22Y-UL3 -0.159236 0.0408475 Min -0.159236 0.0485796 -0.0320817 0.0364794
22Y-UL4 -0.117863 0.104917 Min -0.117863 0.0458407 0.0115418 0.0443639
22Y-UL5 - -
22Y-UL6 -0.0510021 0.663159 Max 0.663159 0.22925 0.1557 0.168266
Missing values highlighted in yellow indicate that tooth was missing from arch and therefore, not
included in sample.
“U” indicates Meshmixer, “X” indicates ODL, and “Y” indicated Neolab.

Abstract (if available)

Abstract Introduction: Computer-aided design and manufacturing of orthodontic retainers from digitally debonded models can be used to facilitate same-day delivery. The purpose of this prospective clinical study was to evaluate the accuracy of virtual bracket removal (VBR) techniques using two orthodontic laboratories and the in-office VBR protocol. The objective was to compare the accuracy of VBR from the two laboratories and the in-house VBR software with conventional bracket removal to provide evidence-based support of clinical use of different VBR systems. ❧ Methods: In this prospective study, the sample consisted of intra-oral scans of 20 patients. There were four groups compared (all subject participants will be included in each group). Group 1 was a control group of post-bracket removal scans/pre-bonding scan. Group 2 consisted of post-VBR scans from in-office, using a previously established protocol with Meshmixer™ software. Group 3 consisted of post-VBR scans from ODL Orthodontic Lab (Buffalo, NY) and Group 4 consisted of post-VBR scans from Neolab (Andover, MA). Scans were taken twice in the same time point before and after bonding or before and after debonding. The scans with and without brackets were obtained on the same day for each patient using the 3Shape Trios scanner. The scans without brackets were used as a control group. The second group of scans underwent VBR using an in-house VBR software, Meshmixer™, using the USC VBR protocol previously established. The scans with brackets were sent to two orthodontic labs, ODL Laboratories, and Neolab, where VBR was performed and the STL scans obtained after VBR. The virtually debonded models were superimposed onto the control models, and 3D Euclidean distances between surface points of superimposed models were calculated for comparative analysis of surface changes due to VBR. Surface changes were expressed via color mapping using VAM software. One-way ANOVA was used to detect the potential differences between three VBR protocols/labs, separate teeth and tooth segments (incisors, canines/premolars, and first molars). Statistical differences between the test groups were further analyzed with Scheffé’s post hoc test (⍺ = 0.05). ❧ Results: Intra- and inter-operator reliability were determined to be high (>0.9). The accuracy of VBR using the Meshmixer™ protocol did not differ significantly from VBR protocols done by the two labs. However, there was a statistically significant difference (P< 0.05) between the two labs, with ODL showing least accuracy. This finding is likely not clinically significant. When comparing the study groups to the control, there was a statistically significant difference between the three tooth segments (incisors, canines/premolars, and first molars), with VBR of the first molars and second premolars showing the least accuracy. ❧ Conclusions: VBR using the Meshmixer™ protocol is comparable to VBR using Neolab or ODL Laboratories. VBR accuracy increases from posterior teeth to anterior teeth. VBR using any of the three protocols is clinically acceptable for computer-aided design and manufacturing of retainers.

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Asset Metadata

Creator Marsh, Kaitlin Anne (author)

Core Title 3D assessment of virtual bracket removal for modern orthodontic retainers: a prospective clinical study

School School of Dentistry

Degree Master of Science

Degree Program Craniofacial Biology

Publication Date 02/26/2020

Defense Date 02/07/2020

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

Tag 3D printing,3D technology,computer aided design,computer aided manufacturing,digital orthodontics,OAI-PMH Harvest,orthodontics,retainers,retention,virtual bracket removal

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Paine, Michael (committee chair), Ahn, Ji Hyun (committee member), Sameshima, Glenn (committee member)

Creator Email kaitliam@usc.edu,kamartian@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-274867

Unique identifier UC11673144

Identifier etd-MarshKaitl-8213.pdf (filename),usctheses-c89-274867 (legacy record id)

Legacy Identifier etd-MarshKaitl-8213.pdf

Dmrecord 274867

Document Type Thesis

Rights Marsh, Kaitlin Anne

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