Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning

Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning

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 collect...

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Bibliographic Details
Published in:Diagnostics (Basel) Vol. 13; no. 16; p. 2713
Main Authors: Zakhar, Grant, Hazime, Samir, Eckert, George, Wong, Ariel, Badirli, Sarkhan, Turkkahraman, Hakan
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-08-2023
MDPI
Subjects:
Accuracy
Algorithms
Artificial intelligence
Automation
Confidence intervals
growth prediction
Longitudinal studies
Machine learning
Males
Malocclusion
mandibular growth
Morphology
Online databases
Orthodontics
Patients
Software
Systematic review
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Summary: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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ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics13162713