ArticleInternational journal of legal medicine2023
Semi-supervised automatic dental age and sex estimation using a hybrid transformer model.
Article in International journal of legal medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Artificial intelligence in age and sex determination using maxillofacial radiographs: A systematic review.The Journal of forensic odonto-stomatology · 2024Pooled it
- Hybrid approach with convolutional neural network and regression models for age estimation in panoramic radiographs.International journal of legal medicine · 2026Observational
- Comprehensive assessment of minors' biological profiles using explainable machine learning: the assessment of age, stature, body mass, and sex.International journal of legal medicine · 2026Article
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- A fully automated deep learning framework for age estimation in adults using periapical radiographs of canine teeth.International journal of legal medicine · 2025Article
- Age Estimation from Lateral Cephalograms Using Deep Learning: A Pilot Study from Early Childhood to Older Adults.Journal of clinical medicine · 2025Article
- A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi children.BMC oral health · 2025Article
- Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population.International journal of legal medicine · 2024Article
- Radiological assessment of periodontal ligament space visibility on third molars for forensic age assessment - a comparison study of three different staging scales.International journal of legal medicine · 2024Article
- Machine learning and deep learning enabled age estimation on medial clavicle CT images.International journal of legal medicine · 2024Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Teeth-based age and sex estimation is an important task in mass disasters, criminal scenes, and archeology. Although various methods have been proposed, most of them are subjective and influenced by observers' experiences. In this study, we aimed to develop a deep learning model for automatic dental age and sex estimation from orthopantomograms (OPGs) and compare to manual methods. A large dataset of 15,195 OPGs (age range, 16 ~ 50 years; mean age, 29.65 years ± 9.36 [SD]; 10,218 females) was used to train and test a hybrid deep learning model which is a combination of convolutional neural network and transformer model. The final performance of this model was evaluated on additional independent 100 OPGs and compared to the manual method for external validation. In the test of 1413 OPGs, the mean absolute error (MAE) of age estimation was 2.61 years by this model. The accuracy and the area under the receiver operating characteristic curve (AUC) of sex estimation were 95.54% and 0.984. The heatmap indicated that the crown and pulp chamber of premolars and molars contain the most age-related information. In the additional independent 100 OPGs, this model achieved an MAE of 3.28 years for males and 3.79 years for females. The accuracy of this model was much higher than that of the manual models. Therefore, this model has the potential to assist radiologists in automated age and sex estimation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.