Using machine learning to determine age over 16 based on development of third molar and periodontal ligament of second molar

Abstract

Abstract Background Having a reliable and feasible method to estimate whether an individual has reached 16 years of age would greatly benefit forensic analysis. The study of age using dental information has matured recently. In addition, machine learning (ML) is gradually being applied for dental age estimation. Aim The purpose of this study was to evaluate the development of the third molar using the Demirjian method (Demirjian3M), measure the development index of the third molar (I3M) using the method by Cameriere, and assess the periodontal ligament development of the second molar (PL2M). This study aimed to predict whether Chinese adolescents have reached the age of criminal responsibility (16 years) by combining the above measurements with ML techniques. Subjects & methods A total of 665 Chinese adolescents aged between 12 and 20 years were recruited for this study. The development of the second and third molars was evaluated by taking orthopantomographs. ML algorithms, including random forests (RF), decision trees (DT), support vector machines (SVM), K-nearest neighbours (KNN), Bernoulli Naive Bayes (BNB), and logistic regression (LR), were used for training and testing to determine the dental age. This is the first study to combine ML with an evaluation of periodontal ligament and tooth development to predict whether individuals are over 16 years of age. Results and conclusions The study showed that SVM had the highest Bayesian posterior probability at 0.917 and a Youden index of 0.752. This finding provides an important reference for forensic identification, and the combination of traditional methods and ML is expected to improve the accuracy of age determination for this population, which is of substantial significance for criminal litigation.

Keywords

• Age determination
• Juvenile criminal age
• Machine learning
• Second molar
• Third molar
• Periodontal ligament