Using machine learning to determine age over 16 based on development of third molar and periodontal ligament of second molar.
Age determination
Juvenile criminal age
Machine learning
Periodontal ligament
Second molar
Third molar
Journal
BMC oral health
ISSN: 1472-6831
Titre abrégé: BMC Oral Health
Pays: England
ID NLM: 101088684
Informations de publication
Date de publication:
20 09 2023
20 09 2023
Historique:
received:
10
04
2023
accepted:
03
08
2023
medline:
22
9
2023
pubmed:
21
9
2023
entrez:
20
9
2023
Statut:
epublish
Résumé
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. The purpose of this study was to evaluate the development of the third molar using the Demirjian method (Demirjian 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. 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.
Sections du résumé
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 (Demirjian
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.
Identifiants
pubmed: 37730591
doi: 10.1186/s12903-023-03284-5
pii: 10.1186/s12903-023-03284-5
pmc: PMC10510268
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
680Informations de copyright
© 2023. BioMed Central Ltd., part of Springer Nature.
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