ArticleThe Journal of forensic odonto-stomatology2024
Machine learning assisted 5-part tooth segmentation method for CBCT-based dental age estimation in adults.
Article in The Journal of forensic odonto-stomatology, 2024. 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.
- Advancements in Dental Science through Artificial Intelligence and Machine Learning: A Comprehensive Review.International journal of clinical pediatric dentistry · 2026Review
- Accuracy and challenges in age estimation in adults: a scoping review of anthropological, dental, biochemical, and molecular methods.Forensic science, medicine, and pathology · 2025Article
- Deep learning-based assessment of pulp involvement in primary molars using YOLO v8.PLOS digital health · 2025Article
- Volumetric analysis of maxillary canine and maxillary sinus for age estimation using CBCT scans: A predictive tool in forensic dentistry.Journal of oral and maxillofacial pathology : JOMFPArticle
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Authors and funding
5 authors.
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Abstract
backgroundThe utilization of segmentation method using volumetric data in adults dental age estimation (DAE) from cone-beam computed tomography (CBCT) was further expanded by using current 5-Part Tooth Segmentation (SG) method. Additionally, supervised machine learning modelling -namely support vector regression (SVR) with linear and polynomial kernel, and regression tree - was tested and compared with the multiple linear regression model. MATERIAL AND
methodsCBCT scans from 99 patients aged between 20 to 59.99 was collected. Eighty eligible teeth including maxillary canine, lateral incisor, and central incisor were used in this study. Enamel to dentine volume ratio, pulp to dentine volume ratio, lower tooth volume ratio, and sex was utilized as independent variable to predict chronological age.
resultsNo multicollinearity was detected in the models. The best performing model comes from maxillary lateral incisor using SVR with polynomial kernel ( = 0.73). The lowest error rate achieved by the model was given also by maxillary lateral incisor, with 4.86 years of mean average error and 6.05 years of root means squared error. However, demands a complex approach to segment the enamel volume in the crown section and a lengthier labour time of 45 minutes per tooth.
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