ArticleInternational journal of legal medicine2024
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.
Article in International journal of legal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in forensic science: a systematic review. Part I: personal identification.International journal of legal medicine · 2026Pooled it
- Deep convolutional neural networks for age and gender estimation using orthopantomogram images: a systematic review.Evidence-based dentistry · 2026Pooled it
- Systematic Review and Meta-Analysis on the Accuracy of Artificial Intelligence Algorithms in Individuals Gender Detection Using Orthopantomograms.International dental journal · 2025Pooled it
- Age estimation from pubic symphysis based on cinematic volume rendering: comparison between Suchey-Brooks staging and deep learning.International journal of legal medicine · 2026Article
- Hybrid approach with convolutional neural network and regression models for age estimation in panoramic radiographs.International journal of legal medicine · 2026Observational
- Deep learning-derived retinal age gap and its associations with lifestyle, systemic, and ocular health in a health screening cohort.GeroScience · 2026Article
- Reliability of the QUADAS-2 checklist in AI-based medical imaging: A comparison of human reviewers and ChatGPT-4o.Imaging science in dentistry · 2026Article
- Age estimation through mandibular radiomic attributes in panoramic radiographs using convolutional neural networks.Imaging science in dentistry · 2026Article
- Multi-task learning for forensic age and sex estimation using large foundation models on pelvic radiographs: a multi-center study.International journal of legal medicine · 2026Article
- Sex estimation from hand-wrist radiographic images: an artificial intelligence approach.International journal of legal medicine · 2026Article
- Artificial Intelligence-Assisted Sex Estimation from Canine Measurements on Panoramic Radiographs: Comparison with Manual Analysis in a Romanian Sample.Diagnostics (Basel, Switzerland) · 2026Article
- Comparative Analysis of Third Molar Segmentation Performance Between Sexes Using Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Article
- Sex estimation from lateral cephalograms via a hybrid multimodel convolutional neural network.Scientific reports · 2026Article
- Combining machine learning algorithms to construct a new method for inferring dental age of children with missing teeth in southern China.International journal of legal medicine · 2026Article
- Digital Dental Radiology and Diagnostics-From 2D to 3D.Australian dental journal · 2025Review
- Accuracy and challenges in age estimation in adults: a scoping review of anthropological, dental, biochemical, and molecular methods.Forensic science, medicine, and pathology · 2025Article
- A fully automated deep learning framework for age estimation in adults using periapical radiographs of canine teeth.International journal of legal medicine · 2025Article
- Comparison of 2D, 2.5D, and 3D segmentation networks for mandibular canals in CBCT images: a study on public and external datasets.BMC oral health · 2025Article
- Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning.Scientific reports · 2025Article
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9 authors.
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Abstract
Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.
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