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Item Embracing the Unprecedented Pace of Change: Artificial Intelligence's Impact on Dentistry and Beyond(Thieme, 2023) Turkkahraman, Hakan; Orthodontics and Oral Facial Genetics, School of DentistryItem 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 DentistryIn 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.Item Short- and Long-Term Prediction of the Post-Pubertal Mandibular Length and Y-Axis in Females Utilizing Machine Learning(MDPI, 2023-08-22) Parrish, Matthew; O’Connell, Ella; Eckert, George; Hughes, Jay; Badirli, Sarkhan; Turkkahraman, Hakan; Orthodontics and Oral Facial Genetics, School of DentistryThe aim of this study was to create a novel machine learning (ML) algorithm for predicting the post-pubertal mandibular length and Y-axis in females. Cephalometric data from 176 females with Angle Class I occlusion were used to train and test seven ML algorithms. For all ML methods tested, the mean absolute errors (MAEs) for the 2-year prediction ranged from 2.78 to 5.40 mm and 0.88 to 1.48 degrees, respectively. For the 4-year prediction, MAEs of mandibular length and Y-axis ranged from 3.21 to 4.00 mm and 1.19 to 5.12 degrees, respectively. The most predictive factors for post-pubertal mandibular length were mandibular length at previous timepoints, age, sagittal positions of the maxillary and mandibular skeletal bases, mandibular plane angle, and anterior and posterior face heights. The most predictive factors for post-pubertal Y-axis were Y-axis at previous timepoints, mandibular plane angle, and sagittal positions of the maxillary and mandibular skeletal bases. ML methods were identified as capable of predicting mandibular length within 3 mm and Y-axis within 1 degree. Compared to each other, all of the ML algorithms were similarly accurate, with the exception of multilayer perceptron regressor.Item Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning(MDPI, 2023-08-21) Zakhar, Grant; Hazime, Samir; Eckert, George; Wong, Ariel; Badirli, Sarkhan; Turkkahraman, Hakan; Orthodontics and Oral Facial Genetics, School of DentistryThe goal of this study was to create a novel machine learning (ML) model that can predict the magnitude and direction of pubertal mandibular growth in males with Class II malocclusion. Lateral cephalometric radiographs of 123 males at three time points (T1: 12; T2: 14; T3: 16 years old) were collected from an online database of longitudinal growth studies. Each radiograph was traced, and seven different ML models were trained using 38 data points obtained from 92 subjects. Thirty-one subjects were used as the test group to predict the post-pubertal mandibular length and y-axis, using input data from T1 and T2 combined (2 year prediction), and T1 alone (4 year prediction). Mean absolute errors (MAEs) were used to evaluate the accuracy of each model. For all ML methods tested using the 2 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.11–6.07 mm to 0.85–2.74° for the y-axis. For all ML methods tested with 4 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.32–5.28 mm to 1.25–1.72° for the y-axis. Besides its initial length, the most predictive factors for mandibular length were found to be chronological age, upper and lower face heights, upper and lower incisor positions, and inclinations. For the y-axis, the most predictive factors were found to be y-axis at earlier time points, SN-MP, SN-Pog, SNB, and SNA. Although the potential of ML techniques to accurately forecast future mandibular growth in Class II cases is promising, a requirement for more substantial sample sizes exists to further enhance the precision of these predictions.Item Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study(MDPI, 2023-04-26) Wood, Tyler; Anigbo, Justina O.; Eckert, George; Stewart, Kelton T.; Dundar, Mehmet Murat; Turkkahraman, Hakan; Orthodontics and Oral Facial Genetics, School of DentistryThe aim was to predict the post-pubertal mandibular length and Y axis of growth in males by using various machine learning (ML) techniques. Cephalometric data obtained from 163 males with Class I Angle malocclusion, were used to train various ML algorithms. Analysis of variances (ANOVA) was used to compare the differences between predicted and actual measurements among methods and between time points. All the algorithms revealed an accuracy range from 95.80% to 97.64% while predicting post-pubertal mandibular length. When predicting the Y axis of growth, accuracies ranged from 96.60% to 98.34%. There was no significant interaction between methods and time points used for predicting the mandibular length (p = 0.235) and Y axis of growth (p = 0.549). All tested ML algorithms accurately predicted the post-pubertal mandibular length and Y axis of growth. The best predictors for the mandibular length were mandibular and maxillary lengths, and lower face height, while they were Y axis of growth, lower face height, and mandibular plane angle for the post-pubertal Y axis of growth. No significant difference was found among the accuracies of the techniques, except the least squares method had a significantly larger error than all others in predicting the Y axis of growth.Item Learning Games: A New Tool for Orthodontic Education(MDPI, 2023-01-22) Khoo, Edmund; Le, Austin; Lipp, Mitchell J.; Orthodontics and Oral Facial Genetics, School of DentistryLearning games that are based on current scientific concepts are underutilized in dental education. This paper explores the relevant science of learning and discusses several principles that are conducive to learning and teaching in an educational setting, namely retrieval practice, feedback, motivation, and engagement. A discussion of learning games in health professional education ensues, followed by a description of relevant best practices in game design for learning. This paper concludes by presenting Dealodontics©, a card game developed at New York University College of Dentistry with the goal of helping second-year dental students review, practice, and apply basic skills relevant to their orthodontics competency requirements.Item 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 DentistryCannabis 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.Item Effects of minor tooth movements on occlusal forces(2014-07-15) Hernandez-Garcia, Manuel B.; Katona, Thomas R.; Eckert, George J.SUMMARY The aim of this study was to assess if minor tooth shifts, common with dental restorations and during orthodontic treatment, can substantially affect occlusal contact forces. Matched pairs of Dentsply Portrait IPN denture teeth with cuspal angulations of 0°, 20°, 33° and 40° were brought into occlusion by a weighted maxillary tooth. Each pair of teeth was positioned in three interocclusal molar relationships (Angle Class I, Class II and Class III) and 5 relative angulations, for a total of 60 control measurements. From each control position, the mandibular tooth, supported by a load cell, was moved 0.2 mm to the mesial, and in turn, 0.2 mm to the buccal. In each configuration, the three-dimensional loads (3 force and 3 moment components) on the lower tooth were measured by the load cell. It was found that the 0.2 mm tooth shifts substantially affected the occlusal contact forces with all interocclusal configurations and cusps. Current clinical concepts and guidelines oversimplify the complexity of the interactions of occlusal contact forces. Because the relationships between occlusal anatomy and tooth loads are so complex, more investigations are needed to establish the full extent of their potential clinical implications.Item Precision and accuracy assessment of single and multicamera three-dimensional photogrammetry compared with direct anthropometry(Allen Press, 2022) Staller, Sable; Anigbo, Justina; Stewart, Kelton; Dutra, Vinicius; Turkkahraman, Hakan; Orthodontics and Oral Facial Genetics, School of DentistryObjectives: To assess the precision and accuracy of single-camera photogrammetry (SCP) and multicamera photogrammetry (MCP) compared with direct anthropometry (DA). Materials and methods: A total of 30 participants were recruited, and 17 soft tissue landmarks were identified and used to complete a total of 16 measurements. Using SCP and MCP, two three-dimensional (3D) images were acquired from each participant. All 3D measurements and direct measurements were measured twice by the same operator to assess intraexaminer repeatability. Intraclass coefficients (ICCs) were used to evaluate intraexaminer repeatability and interexaminer agreement of the methods. Nonparametric bootstrap analyses were used to compare the means of the measurements among the three methods. Results: All three methods showed excellent intraexaminer repeatability (ICCs > 0.90), except interpupillary distance (ICC = 0.86) measured by SCP. Both SCP and MCP showed excellent interexaminer agreement (ICCs > 0.90), except interpupillary distance (ICC = 0.79), left gonion-pogonion (ICC = 0.74), and columella-subnasale-labrale superior angle (ICC = 0.86) measured by SCP. Overall, there was good agreement between methods, except for columella-subnasale-labrale superior angle (ICC = 0.40) between SCP and MCP. Conclusions: Both SCP and MCP techniques were found to be reliable and valid options for 3D facial imaging. SCP produced slightly larger mean values for several measurements, but the differences were within a clinically acceptable range. Because of the larger margin of errors, measurements including the gonial area and subnasale should be assessed with caution.Item Orthodontic and oral health literacy in adults(Public Library of Science, 2022-08-18) McCarlie, V. Wallace, Jr.; Phillips, Morgan E.; Price, Barry D.; Taylor, Peyton B.; Eckert, George J.; Stewart, Kelton T.; Orthodontics and Oral Facial Genetics, School of DentistryObjective: The primary aim of the study was to determine levels of literacy in both oral health and orthodontics in an adult population. The secondary study aim was to investigate differences in literacy between males and females. Methods: Participants included individuals 18 years or older seeking dental treatment at the East Carolina University (ECU) School of Dental Medicine. To determine levels of oral health literacy (OHL) and orthodontic literacy (OrthoL), validated instruments were administered, including the Rapid Estimate of Adult Literacy in Medicine and Dentistry, the Oral Health Literacy Instrument and its separate scales, and a questionnaire on orthodontic literacy. Summary statistics were computed, and statistical significance was set at 0.05. Results: One hundred seventy-two individuals participated in the study and had a mean age of 55.03 (range:18-88). Greater than 70% of the sampled population exhibited inadequate or marginal oral health knowledge. Additionally, greater than 70% of the sample possessed no more than an 8th grade reading level, with regard to basic medical and dental terms. Higher education was weakly associated with higher OrthoL and OHL. Higher age was also weakly associated with lower OrthoL and OHL. Males on average exhibited significantly higher OHL (p < .05) but there were no OrthoL differences between males and females. Dental visit frequency was not associated with OrthoL or OHL. Conclusion: Low levels of OrthoL and OHL were observed in the study. While males demonstrated a higher level of OHL than females, neither age nor the occurrence of dental appointments significantly influenced levels of literacy.