Dental age assessment based on CBCT images using machine learning algorithms

随机森林 机器学习 人工智能 计算机科学 许可证 算法 支持向量机 决策树 操作系统
作者
Rijad Sarić,Jasmin Kevrić,Naida Hadžiabdić,Ahmed Osmanović,Mirsad Kadic,Muzafer Saračević,Dejan Jokić,Vladimir Rajs
出处
期刊:Forensic Science International [Elsevier BV]
卷期号:334: 111245-111245 被引量:14
标识
DOI:10.1016/j.forsciint.2022.111245
摘要

Age estimation has become inordinately significant for human beings for many reasons, such as detecting legal and criminal responsibility and other social events like a marriage license, birth certificate, etc. This paper aims to decide on the most desirable machine learning algorithm (from conventional machine learning algorithms to deep learning) for dental age estimation based on buccal bone level. The database consisted of 150 CBCT images (73 males and 77 females) from an existing base of the Faculty of Dental Medicine with Clinics, University of Sarajevo, aged 20-69. Results were obtained using the Waikato Environment for Knowledge Analysis (Weka), machine learning software in Java. Left and Right Buccal Alveolar Bone Levels are increasing with age, so they showed to be the most important attributes, especially the latter. Random Forest classifier provided the greatest result with the correlation coefficient of 0.803 and the mean absolute error of 6.022. We have also shown that considering sinus-related features can be a significant addition to the databases. Our paper is probably one of the first studies where regression algorithms based on the Support Vector Machines and Random Forest were utilized.
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