ArticleInternational journal of legal medicine2021
Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images.
Article in International journal of legal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05901857 (Assessing the Precision of Convolutional Neural Networks for Dental Age Estimation in an Egyptian Population From Digital Panoramic Radiographs), which is not on this map. Cited by 38 papers, 3 of them syntheses that pooled it.
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Assessing the Precision of Convolutional Neural Networks for Dental Age Estimation in an Egyptian Population From Digital Panoramic Radiographs: A Diagnostic Accuracy Study
Who cites it
38 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review.International journal of legal medicine · 2024Pooled it
- A systematic overview of dental methods for age assessment in living individuals: from traditional to artificial intelligence-based approaches.International journal of legal medicine · 2023Pooled it
- How old are you? A systematic review investigating the relationship between age and mandibular third molar maturity.PloS one · 2023Pooled it
- Hybrid approach with convolutional neural network and regression models for age estimation in panoramic radiographs.International journal of legal medicine · 2026Observational
- Diagnostic performance of Demirjian's third molar maturation stages G, G1, and H for identifying the 18-year age threshold: a sex-specific study.International journal of legal medicine · 2026Article
- Age estimation through mandibular radiomic attributes in panoramic radiographs using convolutional neural networks.Imaging science in dentistry · 2026Article
- Deep learning-based dental age classification from panoramic radiographs: a multi-granularity, multi-model comparative study.BMC oral health · 2026Article
- Automated Dental Age Estimation from Panoramic Radiographs using an Interpretable Deep Learning Model.International journal of legal medicine · 2026Article
- Classification of periapical radiographic findings for root canal therapy decision support using deep neural networks.BMC medical informatics and decision making · 2026Article
- Dental Age-Group Classification from Panoramic Radiographs Using Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2026Article
- Dental age estimation: a scoping review comparing the manual application of the Demirjian method and artificial intelligence modalities.International journal of legal medicine · 2026Article
- Evaluation of the diagnostic reliability of an AI prototype in detecting clinical features from dental photographs-original research.Frontiers in dental medicine · 2026Article
- Radiographic legal age estimation based on third molar development using Machine-learning algorithms.PloS one · 2026Article
- End-to-end vs. human-defined feature extraction: comparing deep learning approaches for age classification using mandibular third molars.The Journal of forensic odonto-stomatology · 2025Article
- Modified Demirjian's method for dental age estimation in Kosovar children and adolescents.Forensic science, medicine, and pathology · 2025Article
- Deep learning-based method for estimating age from periapical radiographs of upper incisors in a Thai population.Imaging science in dentistry · 2025Article
- Deep learning for forensic age estimation using orthopantomograms in children, adolescents, and young adults.European radiology · 2025Article
- Forensic dental age estimation with deep learning: a modified xception model for panoramic X-Ray images.Forensic science, medicine, and pathology · 2025Article
- Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction.Clinical oral investigations · 2025Article
- Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic-Random Forest.Diagnostics (Basel, Switzerland) · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
Age estimation is an important challenge in many fields, including immigrant identification, legal requirements, and clinical treatments. Deep learning techniques have been applied for age estimation recently but lacking performance comparison between manual and machine learning methods based on a large sample of dental orthopantomograms (OPGs). In total, we collected 10,257 orthopantomograms for the study. We derived logistic regression linear models for each legal age threshold (14, 16, and 18 years old) for manual method and developed the end-to-end convolutional neural network (CNN) which classified the dental age directly to compare with the manual method. Both methods are based on left mandibular eight permanent teeth or the third molar separately. Our results show that compared with the manual methods (92.5%, 91.3%, and 91.8% for age thresholds of 14, 16, and 18, respectively), the end-to-end CNN models perform better (95.9%, 95.4%, and 92.3% for age thresholds of 14, 16, and 18, respectively). This work proves that CNN models can surpass humans in age classification, and the features extracted by machines may be different from that defined by human.
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