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. Diagnostics2023, 13, 2713.
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 2023, 13, 2713.
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. Diagnostics2023, 13, 2713.
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 2023, 13, 2713.
Abstract
The goal was to create a novel machine learning (ML) model which 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 7 different ML models were trained using 38 data points obtained from 92 subjects. 31 subjects were used as a test group, to predict 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 2.11-6.07mm and 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 and 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. Whilst 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.
Medicine and Pharmacology, Dentistry and Oral Surgery
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