Department of Orthodontics and Oral Facial Genetics

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A machine learning model for orthodontic extraction/non-extraction decision in a racially and ethnically diverse patient population
(Elsevier, 2023-09) Mason, Taylor; Kelly, Kynnedy M.; Eckert, George; Dean, Jeffrey A.; Dundar, M. Murat; Turkkahraman, Hakan; Orthodontics and Oral Facial Genetics, School of Dentistry
Introduction The purpose of the present study was to create a machine learning (ML) algorithm with the ability to predict the extraction/non-extraction decision in a racially and ethnically diverse sample. Methods Data was gathered from the records of 393 patients (200 non-extraction and 193 extraction) from a racially and ethnically diverse population. Four ML models (logistic regression [LR], random forest [RF], support vector machine [SVM], and neural network [NN]) were trained on a training set (70% of samples) and then tested on the remaining samples (30%). The accuracy and precision of the ML model predictions were calculated using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. The proportion of correct extraction/non-extraction decisions was also calculated. Results The LR, SVM, and NN models performed best, with an AUC of the ROC of 91.0%, 92.5%, and 92.3%, respectively. The overall proportion of correct decisions was 82%, 76%, 83%, and 81% for the LR, RF, SVM, and NN models, respectively. The features found to be most helpful to the ML algorithms in making their decisions were maxillary crowding/spacing, L1-NB (mm), U1-NA (mm), PFH:AFH, and SN-MP(̊), although many other features contributed significantly. Conclusions ML models can predict the extraction decision in a racially and ethnically diverse patient population with a high degree of accuracy and precision. Crowding, sagittal, and vertical characteristics all featured prominently in the hierarchy of components most influential to the ML decision-making process.
A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration
(MDPI, 2023-08-23) Volovic, James; Badirl, Sarkhan; Ahmad, Sunna; Leavit, Landon; Mason, Taylor; Bhamidipalli, Surya Sruthi; Eckert, George; Albright, David; Turkkahraman, Hakan; Orthodontics and Oral Facial Genetics, School of Dentistry
In the field of orthodontics, providing patients with accurate treatment time estimates is of utmost importance. As orthodontic practices continue to evolve and embrace new advancements, incorporating machine learning (ML) methods becomes increasingly valuable in improving orthodontic diagnosis and treatment planning. This study aimed to develop a novel ML model capable of predicting the orthodontic treatment duration based on essential pre-treatment variables. Patients who completed comprehensive orthodontic treatment at the Indiana University School of Dentistry were included in this retrospective study. Fifty-seven pre-treatment variables were collected and used to train and test nine different ML models. The performance of each model was assessed using descriptive statistics, intraclass correlation coefficients, and one-way analysis of variance tests. Random Forest, Lasso, and Elastic Net were found to be the most accurate, with a mean absolute error of 7.27 months in predicting treatment duration. Extraction decision, COVID, intermaxillary relationship, lower incisor position, and additional appliances were identified as important predictors of treatment duration. Overall, this study demonstrates the potential of ML in predicting orthodontic treatment duration using pre-treatment variables.
Accuracy of 3D Reconstructed Orthodontic Models
(2014) Stewart, Kelton; Chai, Billy; Liu, Sean; Ghoneima, Ahmed; Alford, Timothy
Objective: To evaluate the accuracy of 3D reconstructed orthodontic models, derived by various digitization methods, as compared to conventional orthodontic plaster models. Materials and Methods: Twenty-five maxillary orthodontic plaster models were randomly selected from the Indiana University School of Dentistry Department of Orthodontics. Each plaster model was scanned with the Cadent iOC scanner and the digital data was used to print 3D reconstructed orthodontic models. The same 25 plaster models were duplicated using alginate and poured in plaster after two days. These duplicated plaster models were also scanned with the iOC scanner and 3D reconstructed. Next, the duplicate plaster models were sent to a lab, scanned with a 3Shape R700 scanner, and the digital data was 3D reconstructed. Digital calipers were used to obtain ten linear dimensional measurements on all plaster and 3D reconstructed models for comparisons. Equivalence testing was performed using 2 one-sided paired t-tests with a significance level of P <0.05. Results: Nine of the 10 linear measurements were statistically equivalent in all groups. Clinically insignificant, but statistically significant, measurement differences in maxillary central incisor height (P <0.05) were found on 3D reconstructed models derived from the 3Shape R700 desktop scanner. Conclusion: 3D reconstructed orthodontic models derived from alginate impressions, iOC scanners, and 3Shape R700 scanners are an accurate and reliable substitute for orthodontic plaster models.
Accuracy of Orthodontic Soft Tissue Prediction Software between Different Ethnicities
(2019) Stewart, Kelton; Patel, Pranali; Eckert, George; Rigsbee III, OH; Hughes, Jay; Utreja, Achint
Objective: The objective of this study was to assess the accuracy of the soft tissue prediction module of Dolphin Imaging Software (DIS) in patients requiring extractions as part of the orthodontic treatment plan and compare its accuracy between different ethnicities. Materials and Methods: Initial and final records of 57 patients from three ethnic groups (African Americans, Caucasians, and Hispanics) who completed orthodontic treatment were included for assessment. The identified cases were managed non-surgically with dental extractions. A predictive profile was generated using DIS and compared to post-treatment lateral photographs. Actual and predictive profile photographs were compared using five designated parameters. The assessment parameters were evaluated using a manual protractor. ANOVA was used to compare differences between actual and predicted parameters between the specified groups and ICC was used to assess correlations between the data. Results: Neither ethnicity nor gender had a significant effect on the difference between predicted and final values. No significant difference was noted between the predicted and final images for the nasolabial angle. Significant differences were observed for the mentolabial fold, upper lip to E-line, and lower lip to E-line between predicted and actual images. Additionally, soft tissue convexity was significantly different (p=0.019). Additionally, a clinically significant difference was found for the mentolabial fold. Conclusion: Ethnicity and gender had no impact on the accuracy of predicted and actual image parameters. Overall, DIS demonstrated acceptable accuracy when simulating soft tissue changes after extraction therapy. Additional research on the accuracy of the software is warranted.
Advanced Dental Specialty: Orthodontics Student Manual
(2022) Stewart, Kelton; Kula, Katherine
Formal dental graduate programs should clearly specific the mission, goals, and expectations that a program has of its prospective and current students. Clearly, delineating these points will aid in creating a fair, transparent, and humanistic educational environment. The provided manual outlines key information that educators and programs could include when seeking to establish an effective and impartial educational experience for their students.
An analytical approach to 3D orthodontic load systems
(The Angle Orthodontist, 2014-09) Katona, Thomas R.; Isikbay, Serkis C.; Chen, Jie; Department of Orthodontics and Oral Facial Genetics, IU School of Dentistry
OBJECTIVE: To present and demonstrate a pseudo three-dimensional (3D) analytical approach for the characterization of orthodontic load (force and moment) systems. MATERIALS AND METHODS: Previously measured 3D load systems were evaluated and compared using the traditional two-dimensional (2D) plane approach and the newly proposed vector method. RESULTS: Although both methods demonstrated that the loop designs were not ideal for translatory space closure, they did so for entirely different and conflicting reasons. CONCLUSIONS: The traditional 2D approach to the analysis of 3D load systems is flawed, but the established 2D orthodontic concepts can be substantially preserved and adapted to 3D with the use of a modified coordinate system that is aligned with the desired tooth translation.
The association of malocclusion and trumpet performance
(2015) Kula, Katherine; Cilingir, H. Zeynep; Eckert, George; Dagg, Jack; Ghoniema, Ahmed; Department of Orthodontics and Oral Facial Genetics, School of Dentistry
Objective: To determine whether trumpet performance skills are associated with malocclusion. Materials and Methods: Following institutional review board approval, 70 university trumpet students (54 male, 16 female; aged 20–38.9 years) were consented. After completing a survey, the students were evaluated while playing a scripted performance skills test (flexibility, articulation, range, and endurance exercises) on their instrument in a soundproof music practice room. One investigator (trumpet teacher) used a computerized metronome and a decibel meter during evaluation. A three-dimensional (3D) cone-beam computerized tomography scan (CBCT) was taken of each student the same day as the skills test. Following reliability studies, multiple dental parameters were measured on the 3D CBCT. Nonparametric correlations (Spearman), accepting P < .05 as significant, were used to determine if there were significant associations between dental parameters and the performance skills. Results: Intrarater reliability was excellent (intraclass correlations; all r values > .94). Although associations were weak to moderate, significant negative associations (r ≤ −.32) were found between Little's irregularity index, interincisal inclination, maxillary central incisor rotation, and various flexibility and articulation performance skills, whereas significant positive associations (r ≤ .49) were found between arch widths and various skills. Conclusions: Specific malocclusions are associated with trumpet performance of experienced young musicians.
Associations between Oral Health and Cannabis Use among Adolescents and Young Adults: Implications for Orthodontists
(MDPI, 2022-11-18) Le, Austin; Khoo, Edmund; Palamar, Joseph J.; Orthodontics and Oral Facial Genetics, School of Dentistry
Cannabis use is prevalent among adolescents and young adults in the US. Virtually all modes of cannabis consumption involve the oral cavity, and previous studies have linked cannabis use with poorer oral health. We sought to identify associations between cannabis use and various oral health outcomes and behaviors among individuals 12–25 years of age, and to discuss implications for orthodontists who largely interact with this age group over an extended period of treatment time. We examined data from patient electronic health records (N = 14,657) obtained between 2015 and 2021. Associations between lifetime and current self-reported cannabis use and several oral health outcomes or related behaviors that reflect periodontal health, caries status, oral lesions, and physical integrity of tooth structure and restorations were examined in a bivariable and multivariable manner, controlling for patient age, sex, and self-reported tobacco and alcohol use. Reporting lifetime cannabis use was associated with higher risk for having oral lesions (aPR = 1.41, 95% CI: 1.07–1.85), bruxism (aPR = 1.31, 95% CI: 1.09–1.58), and frequent consumption of sugary beverages and snacks (aPR = 1.27, 95% CI: 1.12–1.41). Reporting current cannabis use was associated with higher risk for oral lesions (aPR = 1.45, 95% CI: 1.03–2.06) and frequent consumption of sugary beverages and snacks (aPR = 1.26, 95% CI: 1.07–1.48). Cannabis users aged 12–25 are at increased risk for bruxism, oral lesions, and frequent consumption of sugary beverages and snacks. Orthodontists and other dental professionals should probe for drug use and be cognizant of increased risk for oral health problems in patients that report actively using cannabis.
AVILA TMD SCORE
(2023-02-03) Avila, Harold
A method for using the prevalence of Temporomandibular Joint Disorder (TMD) Signs, Symptoms and Severe/Chronic Pain to position a patient within the general population. Use of a single digit (0, 1, 2 or 3) to quickly identify the prevalence of Signs, Symptoms and Severe/Chronic Pain of a patient within the general population.
Bimaxillary Protrusion with an Atrophic Alveolar Defect: Orthodontics, Autogenous Chin-Block Graft, Soft Tissue Augmentation, and an Implant
(Elsevier, 2015-01) Chiu, Grace; Chang, Chris; Roberts, W. Eugene; Department of Orthodontics and Oral Facial Genetics
Bimaxillary protrusion in a 28 yr female was complicated by multiple missing, restoratively compromised or hopeless teeth. The maxillary right central incisor (#8) had a history of avulsion and replantation, that subsequently evolved into generalized external root resorption with Class III mobility and a severe loss of supporting periodontium. This complex malocclusion had a Discrepancy Index (DI) of 21, and 8 additional points were scored for the atrophic dental implant site (#8). The comprehensive treatment plan was extraction of four teeth (# 5, 8, 12 & 30), orthodontic closure of all space except for the future implant site (#8), augmentation of the alveolar defect with a autogenous chin- block graft, enhancement of the gingival biotype with a connective tissue graft, and an implant-supported prosthesis. Orthodontists must understand the limitations of bone grafts. Augmented alveolar defects are slow to completely turn over to living bone, so they are usually good sites for implants, but respond poorly to orthodontic space closure. However, postsurgical orthodontics treatment is often indicated to optimally finish the esthetic zone prior to placing the final prosthesis. The latter was effectively performed for the present patient resulting in a total treatment time of ~36 months for comprehensive, interdisciplinary care. An excellent functional and esthetic result was achieved, as documented by a Cast-Radiograph Evaluation (CRE) score of 21 and a Pink & White dental esthetics score of 2.

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