SynthesisClinical oral investigations2024
Deep learning methods for fully automated dental age estimation on orthopantomograms.
Synthesis in Clinical oral investigations, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Dental age estimation in forensic odontology: a systematic review comparing traditional methods and artificial intelligence approaches.Frontiers in dental medicine · 2026Pooled it
- Hybrid approach with convolutional neural network and regression models for age estimation in panoramic radiographs.International journal of legal medicine · 2026Observational
- 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 Tooth Detection and Caries Identification in CBCT With Deep Learning.International dental journal · 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
- Dental age estimation by comparing Demirjian's method and machine learning in Southeast Brazilian youth.Forensic science, medicine, and pathology · 2026Article
- Clinically interpretable deep learning for pediatric dental age estimation with explainable AI.BMC oral health · 2026Article
- Validation of the Dental Age app for automated dental age estimation using Demirjian's method.Frontiers in dental medicine · 2026Article
- Comparative evaluation of ChatGPT and Gemini in detecting external apical root resorption on panoramic radiographs of orthodontic patients : Artificial intelligence performance in detecting root resorption.Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie · 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
- Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic-Random Forest.Diagnostics (Basel, Switzerland) · 2025Article
- The role of artificial intelligence in diagnosing pediatric dental disorders-a narrative review.Frontiers in dental medicine · 2025Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesThis study aimed to use all permanent teeth as the target and establish an automated dental age estimation method across all developmental stages of permanent teeth, accomplishing all the essential steps of tooth determination, tooth development staging, and dental age assessment.
methodsA three-step framework for automatically estimating dental age was developed for children aged 3 to 15. First, a YOLOv3 network was employed to complete the tasks of tooth localization and numbering on a digital orthopantomogram. Second, a novel network named SOS-Net was established for accurate tooth development staging based on a modified Demirjian method. Finally, the dental age assessment procedure was carried out through a single-group meta-analysis utilizing the statistical data derived from our reference dataset.
resultsThe performance tests showed that the one-stage YOLOv3 detection network attained an overall mean average precision 50 of 97.50 for tooth determination. The proposed SOS-Net method achieved an average tooth development staging accuracy of 82.97% for a full dentition. The dental age assessment validation test yielded an MAE of 0.72 years with a full dentition (excluding the third molars) as its input.
conclusionsThe proposed automated framework enhances the dental age estimation process in a fast and standard manner, enabling the reference of any accessible population. CLINICAL RELEVANCE: The tooth development staging network can facilitate the precise identification of permanent teeth with abnormal growth, improving the effectiveness and comprehensiveness of dental diagnoses using pediatric orthopantomograms.
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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.