ArticleBMC oral health2023
Using machine learning to determine age over 16 based on development of third molar and periodontal ligament of second molar.
Article in BMC oral health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine learning-assisted analysis of saliva-containing mixtures using a saliva-specific multi-CpG-microhaplotype marker system.International journal of legal medicine · 2026Article
- Radiographic legal age estimation based on third molar development using Machine-learning algorithms.PloS one · 2026Article
- Neural Network Technologies for Age Estimation in Children from Orthopantomograms (a Pilot Study).Sovremennye tekhnologii v meditsine · 2026Article
- Application of a new composite genetic marker semen-specific methylation-microhaplotype in the analysis of semen-vaginal fluid mixtures.Royal Society open science · 2025Article
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6 authors.
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Abstract
backgroundHaving 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.
aimThe purpose of this study was to evaluate the development of the third molar using the Demirjian method (Demirjian SUBJECTS &
methodsA 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
conclusionsThe 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.
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