دورية أكاديمية
Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning
| العنوان: | Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning |
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| المؤلفون: | Grant Zakhar, Samir Hazime, George Eckert, Ariel Wong, Sarkhan Badirli, Hakan Turkkahraman |
| المصدر: | Diagnostics, Vol 13, Iss 16, p 2713 (2023) |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Medicine (General) |
| مصطلحات موضوعية: | artificial intelligence, machine learning, mandibular growth, growth prediction, Medicine (General), R5-920 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 13162713 2075-4418 |
| Relation: | https://www.mdpi.com/2075-4418/13/16/2713; https://doaj.org/toc/2075-4418 |
| DOI: | 10.3390/diagnostics13162713 |
| URL الوصول: | https://doaj.org/article/d94e69bc300946d99b07068a7e87124a |
| رقم الأكسشن: | edsdoj.94e69bc300946d99b07068a7e87124a |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 13162713 20754418 |
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| DOI: | 10.3390/diagnostics13162713 |