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Wolf D, Farrag G, Flügge T, Timm LH. Predicting Outcome in Clear Aligner Treatment: A Machine Learning Analysis. J Clin Med 2024; 13:3672. [PMID: 38999238 PMCID: PMC11242237 DOI: 10.3390/jcm13133672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 06/11/2024] [Accepted: 06/20/2024] [Indexed: 07/14/2024] Open
Abstract
Background/Objectives: Machine learning (ML) models predicting the risk of refinement (i.e., a subsequent course of treatment being necessary) in clear aligner therapy (CAT) were developed and evaluated. Methods: An anonymized sample of 9942 CAT patients (70.6% females, 29.4% males, age range 18-64 years, median 30.5 years), as provided by DrSmile, a large European CAT provider based in Berlin, Germany, was used. Three different ML methods were employed: (1) logistic regression with L1 regularization, (2) extreme gradient boosting (XGBoost), and (3) support vector classification with a radial basis function kernel. In total, 74 factors were selected as predictors for these methods and are consistent with clinical reasoning. Results: On a held-out test set with a true-positive rate of 0.58, the logistic regression model has an area under the ROC curve (AUC) of 0.67, an average precision (AP) of 0.73, and Brier loss of 0.22; the XGBoost model has an AUC of 0.67, an AP of 0.74, and Brier loss of 0.22; and the support vector model has a recall of 0.61 and a precision of 0.64. The logistic regression and XGBoost models identify predictors influencing refinement risk, including patient compliance, interproximal enamel reduction (IPR) and certain planned tooth movements, for example, lingual translation of maxillary incisors being associated with the lowest risk of refinement and rotation of mandibular incisors with the highest risk. Conclusions: These findings suggest moderate, well-calibrated predictive accuracy with both regularized logistic regression and XGBoost and underscore the influence the identified factors have on the risk of refinement in CAT, emphasizing their importance in the careful planning of orthodontic treatment and the potential for shorter treatment times, less patient discomfort, and fewer clinic visits. Identification of at-risk individuals could support tailored clinical decision-making and enable targeted interventions.
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Affiliation(s)
- Daniel Wolf
- Independent Researcher, Berlin 13089, Germany
| | - Gasser Farrag
- Straumann Group-etkon GmbH, Lochhamer Schlag 6, 82166 Gräfelfing, Germany
| | - Tabea Flügge
- Department of Oral and Maxillofacial Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Lan Huong Timm
- DrSmile-DZK Deutsche Zahnklinik GmbH, Königsallee 92a, 40212 Düsseldorf, Germany
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Surendran A, Daigavane P, Shrivastav S, Kamble R, Sanchla AD, Bharti L, Shinde M. The Future of Orthodontics: Deep Learning Technologies. Cureus 2024; 16:e62045. [PMID: 38989357 PMCID: PMC11234326 DOI: 10.7759/cureus.62045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 06/09/2024] [Indexed: 07/12/2024] Open
Abstract
Deep learning has emerged as a revolutionary technical advancement in modern orthodontics, offering novel methods for diagnosis, treatment planning, and outcome prediction. Over the past 25 years, the field of dentistry has widely adopted information technology (IT), resulting in several benefits, including decreased expenses, increased efficiency, decreased need for human expertise, and reduced errors. The transition from preset rules to learning from real-world examples, particularly machine learning (ML) and artificial intelligence (AI), has greatly benefited the organization, analysis, and storage of medical data. Deep learning, a type of AI, enables robots to mimic human neural networks, allowing them to learn and make decisions independently without the need for explicit programming. Its ability to automate cephalometric analysis and enhance diagnosis through 3D imaging has revolutionized orthodontic operations. Deep learning models have the potential to significantly improve treatment outcomes and reduce human errors by accurately identifying anatomical characteristics on radiographs, thereby expediting analytical processes. Additionally, the use of 3D imaging technologies such as cone-beam computed tomography (CBCT) can facilitate precise treatment planning, allowing for comprehensive examinations of craniofacial architecture, tooth movements, and airway dimensions. In today's era of personalized medicine, deep learning's ability to customize treatments for individual patients has propelled the field of orthodontics forward tremendously. However, it is essential to address issues related to data privacy, model interpretability, and ethical considerations before orthodontic practices can use deep learning in an ethical and responsible manner. Modern orthodontics is evolving, thanks to the ability of deep learning to deliver more accurate, effective, and personalized orthodontic treatments, improving patient care as technology develops.
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Affiliation(s)
- Aathira Surendran
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Pallavi Daigavane
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Sunita Shrivastav
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Ranjit Kamble
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Abhishek D Sanchla
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Lovely Bharti
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Mrudula Shinde
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
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Köktürk B, Pamukçu H, Gözüaçık Ö. Evaluation of different machine learning algorithms for extraction decision in orthodontic treatment. Orthod Craniofac Res 2024. [PMID: 38764408 DOI: 10.1111/ocr.12811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 05/21/2024]
Abstract
INTRODUCTION The extraction decision significantly affects the treatment process and outcome. Therefore, it is crucial to make this decision with a more objective and standardized method. The objectives of this study were (1) to identify the best-performing model among seven machine learning (ML) models, which will standardize the extraction decision and serve as a guide for inexperienced clinicians, and (2) to determine the important variables for the extraction decision. METHODS This study included 1000 patients who received orthodontic treatment with or without extraction (500 extraction and 500 non-extraction). The success criteria of the study were the decisions made by the four experienced orthodontists. Seven ML models were trained using 36 variables; including demographic information, cephalometric and model measurements. First, the extraction decision was performed, and then the extraction type was identified. Accuracy and area under the curve (AUC) of the receiver operating characteristics (ROC) curve were used to measure the success of ML models. RESULTS The Stacking Classifier model, which consists of Gradient Boosted Trees, Support Vector Machine, and Random Forest models, showed the highest performance in extraction decision with 91.2% AUC. The most important features determining extraction decision were maxillary and mandibular arch length discrepancy, Wits Appraisal, and ANS-Me length. Likewise, the Stacking Classifier showed the highest performance with 76.3% accuracy in extraction type decisions. The most important variables for the extraction type decision were mandibular arch length discrepancy, Class I molar relationship, cephalometric overbite, Wits Appraisal, and L1-NB distance. CONCLUSION The Stacking Classifier model exhibited the best performance for the extraction decision. While ML models showed a high performance in extraction decision, they could not able to achieve the same level of performance in extraction type decision.
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Affiliation(s)
- Begüm Köktürk
- Department of Orthodontics, Faculty of Dentistry, Başkent University, Ankara, Turkey
| | - Hande Pamukçu
- Department of Orthodontics, Faculty of Dentistry, Başkent University, Ankara, Turkey
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Lee JM, Moon JH, Park JA, Kim JH, Lee SJ. Factors influencing the development of artificial intelligence in orthodontics. Orthod Craniofac Res 2024. [PMID: 38712670 DOI: 10.1111/ocr.12806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/23/2024] [Indexed: 05/08/2024]
Abstract
OBJECTIVES Since developing AI procedures demands significant computing resources and time, the implementation of a careful experimental design is essential. The purpose of this study was to investigate factors influencing the development of AI in orthodontics. MATERIALS AND METHODS A total of 162 AI models were developed, with various combinations of sample sizes (170, 340, 679), input variables (40, 80, 160), output variables (38, 76, 154), training sessions (100, 500, 1000), and computer specifications (new vs. old). The TabNet deep-learning algorithm was used to develop these AI models, and leave-one-out cross-validation was applied in training. The goodness-of-fit of the regression models was compared using the adjusted coefficient of determination values, and the best-fit model was selected accordingly. Multiple linear regression analyses were employed to investigate the relationship between the influencing factors. RESULTS Increasing the number of training sessions enhanced the effectiveness of the AI models. The best-fit regression model for predicting the computational time of AI, which included logarithmic transformation of time, sample size, and training session variables, demonstrated an adjusted coefficient of determination of 0.99. CONCLUSION The study results show that estimating the time required for AI development may be possible using logarithmic transformations of time, sample size, and training session variables, followed by applying coefficients estimated through several pilot studies with reduced sample sizes and reduced training sessions.
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Affiliation(s)
| | | | - Ji-Ae Park
- Department of Orthodontics, Seoul National University Dental Hospital, Seoul, Korea
| | | | - Shin-Jae Lee
- Department of Orthodontics and Dental Research Institute, Seoul National University School of Dentistry, Seoul, Korea
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Allareddy V, Oubaidin M, Rampa S, Venugopalan SR, Elnagar MH, Yadav S, Lee MK. Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health. Orthod Craniofac Res 2023; 26 Suppl 1:124-130. [PMID: 37846615 DOI: 10.1111/ocr.12721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/09/2023] [Indexed: 10/18/2023]
Abstract
Machine Learning (ML), a subfield of Artificial Intelligence (AI), is being increasingly used in Orthodontics and craniofacial health for predicting clinical outcomes. Current ML/AI models are prone to accentuate racial disparities. The objective of this narrative review is to provide an overview of how AI/ML models perpetuate racial biases and how we can mitigate this situation. A narrative review of articles published in the medical literature on racial biases and the use of AI/ML models was undertaken. Current AI/ML models are built on homogenous clinical datasets that have a gross underrepresentation of historically disadvantages demographic groups, especially the ethno-racial minorities. The consequence of such AI/ML models is that they perform poorly when deployed on ethno-racial minorities thus further amplifying racial biases. Healthcare providers, policymakers, AI developers and all stakeholders should pay close attention to various steps in the pipeline of building AI/ML models and every effort must be made to establish algorithmic fairness to redress inequities.
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Affiliation(s)
- Veerasathpurush Allareddy
- Department of Orthodontics, University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA
| | - Maysaa Oubaidin
- Department of Orthodontics, University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA
| | - Sankeerth Rampa
- Health Care Administration Program, School of Business, Rhode Island College, Providence, Rhode Island, USA
| | | | - Mohammed H Elnagar
- Department of Orthodontics, University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA
| | - Sumit Yadav
- Department of Orthodontics, University of Nebraska Medical Center, Lincoln, Nebraska, USA
| | - Min Kyeong Lee
- Department of Orthodontics, University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA
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Liu J, Zhang C, Shan Z. Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives. Healthcare (Basel) 2023; 11:2760. [PMID: 37893833 PMCID: PMC10606213 DOI: 10.3390/healthcare11202760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/11/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide a comprehensive overview of the present state of AI applications in orthodontics, which can be categorized into the following domains: (1) diagnosis, including cephalometric analysis, dental analysis, facial analysis, skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, including decision making for extractions and orthognathic surgery, and treatment outcome prediction; and (3) clinical practice, including practice guidance, remote care, and clinical documentation. We have witnessed a broadening of the application of AI in orthodontics, accompanied by advancements in its performance. Additionally, this review outlines the existing limitations within the field and offers future perspectives.
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Affiliation(s)
- Junqi Liu
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Chengfei Zhang
- Division of Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Zhiyi Shan
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
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Mason T, Kelly KM, Eckert G, Dean JA, Dundar MM, Turkkahraman H. A machine learning model for orthodontic extraction/non-extraction decision in a racially and ethnically diverse patient population. Int Orthod 2023; 21:100759. [PMID: 37196482 DOI: 10.1016/j.ortho.2023.100759] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/27/2023] [Accepted: 04/30/2023] [Indexed: 05/19/2023]
Abstract
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.
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Affiliation(s)
- Taylor Mason
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN, US
| | - Kynnedy M Kelly
- Indiana University School of Dentistry, Indianapolis, IN, US
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indianapolis, Indiana University School of Medicine, IN, US
| | - Jeffrey A Dean
- Department of Pediatric Dentistry, Indiana University School of Dentistry, Indianapolis, IN, US
| | - M Murat Dundar
- Department of Computer & Information Science, Indiana University Purdue University at Indianapolis, School of Science, Indianapolis, IN, US
| | - Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN, US.
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Volovic J, Badirli S, Ahmad S, Leavitt L, Mason T, Bhamidipalli SS, Eckert G, Albright D, Turkkahraman H. A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration. Diagnostics (Basel) 2023; 13:2740. [PMID: 37685278 PMCID: PMC10486486 DOI: 10.3390/diagnostics13172740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/14/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
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.
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Affiliation(s)
- James Volovic
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (J.V.); (S.A.); (D.A.)
| | | | - Sunna Ahmad
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (J.V.); (S.A.); (D.A.)
| | - Landon Leavitt
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (J.V.); (S.A.); (D.A.)
| | - Taylor Mason
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (J.V.); (S.A.); (D.A.)
| | - Surya Sruthi Bhamidipalli
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (S.S.B.); (G.E.)
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (S.S.B.); (G.E.)
| | - David Albright
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (J.V.); (S.A.); (D.A.)
| | - Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (J.V.); (S.A.); (D.A.)
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Parrish M, O’Connell E, Eckert G, Hughes J, Badirli S, Turkkahraman H. Short- and Long-Term Prediction of the Post-Pubertal Mandibular Length and Y-Axis in Females Utilizing Machine Learning. Diagnostics (Basel) 2023; 13:2729. [PMID: 37685267 PMCID: PMC10486405 DOI: 10.3390/diagnostics13172729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/07/2023] [Accepted: 08/20/2023] [Indexed: 09/10/2023] Open
Abstract
The 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.
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Affiliation(s)
- Matthew Parrish
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA; (M.P.); (J.H.)
| | - Ella O’Connell
- Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA;
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Jay Hughes
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA; (M.P.); (J.H.)
| | | | - Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA; (M.P.); (J.H.)
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Zakhar G, Hazime S, Eckert G, Wong A, Badirli S, Turkkahraman H. Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning. Diagnostics (Basel) 2023; 13:2713. [PMID: 37627972 PMCID: PMC10453460 DOI: 10.3390/diagnostics13162713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/11/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023] Open
Abstract
The 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.
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Affiliation(s)
- Grant Zakhar
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (G.Z.); (A.W.)
| | - Samir Hazime
- Indiana University School of Dentistry, Indianapolis, IN 46202, USA;
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Ariel Wong
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (G.Z.); (A.W.)
| | | | - Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (G.Z.); (A.W.)
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Wood T, Anigbo JO, Eckert G, Stewart KT, Dundar MM, Turkkahraman H. Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study. Diagnostics (Basel) 2023; 13:diagnostics13091553. [PMID: 37174945 PMCID: PMC10178146 DOI: 10.3390/diagnostics13091553] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/18/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023] Open
Abstract
The 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.
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Affiliation(s)
- Tyler Wood
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA
| | - Justina O Anigbo
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Kelton T Stewart
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA
| | - Mehmet Murat Dundar
- Department of Computer & Information Science, Indiana University Purdue University at Indianapolis School of Science, Indianapolis, IN 46202, USA
| | - Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA
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