ArticleFrontiers in dental medicine2025
Automatic dental age estimation in adolescents via oral panoramic imaging.
Article in Frontiers in dental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Dental age estimation in forensic odontology: a systematic review comparing traditional methods and artificial intelligence approaches.Frontiers in dental medicine · 2026Pooled it
- First-stage assessment of AI-based anatomical measurement accuracy for the Cameriere European dental age estimation method.International journal of legal medicine · 2026Article
- Machine learning approach for adult age estimation using dental characteristics on panoramic radiographs.Scientific reports · 2026Article
- Deep learning-based dental age estimation in adolescents using panoramic radiographs: a comparative study with traditional methods.Frontiers in radiology · 2026Article
- Deep learning-based method for estimating age from periapical radiographs of upper incisors in a Thai population.Imaging science in dentistry · 2025Article
- Dental Age Estimation from Panoramic Radiographs: A Comparison of Orthodontist and ChatGPT-4 Evaluations Using the London Atlas, Nolla, and Haavikko Methods.Diagnostics (Basel, Switzerland) · 2025Article
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8 authors.
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Abstract
Object: In forensic dentistry, dental age estimation assists experts in determining the age of victims or suspects, which is vital for legal responsibility and sentencing. The traditional Demirjian method assesses the development of seven mandibular teeth in pediatric dentistry, but it is time-consuming and relies heavily on subjective judgment. Methods: This study constructed a largescale panoramic dental image dataset and applied various convolutional neural network (CNN) models for automated age estimation. Results: Model performance was evaluated using loss curves, residual histograms, and normal PP plots. Age prediction models were built separately for the total, female, and male samples. The best models yielded mean absolute errors of 1.24, 1.28, and 1.15 years, respectively. Discussion: These findings confirm the effectiveness of deep learning models in dental age estimation, particularly among northern Chinese adolescents.
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