ArticleBMC oral health2024
Fully automated deep learning approach to dental development assessment in panoramic radiographs.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Explainable artificial intelligence in dental imaging: a systematic review of interpretability and the current state of trust evidence.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
- Hybrid approach with convolutional neural network and regression models for age estimation in panoramic radiographs.International journal of legal medicine · 2026Observational
- Reliability of the QUADAS-2 checklist in AI-based medical imaging: A comparison of human reviewers and ChatGPT-4o.Imaging science in dentistry · 2026Article
- Comparative Analysis of Third Molar Segmentation Performance Across Dental Developmental Stages and the 18-Year Age Threshold Using Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Article
- Automated prediction of hand-wrist maturation stage from lateral cephalometric and panoramic radiographs using a deep learning approach.European journal of orthodontics · 2026Article
- Deep‑learning‑based detection of open‑apex teeth on panoramic radiographs using YOLO models.Oral radiology · 2026Article
- Deep learning-based dental age classification from panoramic radiographs: a multi-granularity, multi-model comparative study.BMC oral health · 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
- 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
- Classification of pediatric dental diseases from panoramic radiographs using natural language transformer and deep learning models.Frontiers in artificial intelligence · 2026Article
- Validation of the Dental Age app for automated dental age estimation using Demirjian's method.Frontiers in dental medicine · 2026Article
- Modified Demirjian's method for dental age estimation in Kosovar children and adolescents.Forensic science, medicine, and pathology · 2025Article
- Artificial intelligence versus human expertise: reliability of ChatGPT and the London atlas for dental age estimation using panoramic radiographs.BMC oral health · 2025Article
- An autoencoder and vision transformer based interpretability analysis on the performance differences in automated staging of second and third molars.Scientific reports · 2025Article
- Development and evaluation of a deep learning-based system for dental age estimation using the demirjian method on panoramic radiographs.BMC oral health · 2025Article
- Automatic detection of developmental stages of molar teeth with deep learning.BMC oral health · 2025Article
- Evaluation of the Performance of a YOLOv10-Based Deep Learning Model for Tooth Detection and Numbering on Panoramic Radiographs of Patients in the Mixed Dentition Period.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence and dental age estimation: development and validation of an automated stage allocation technique on all mandibular tooth types in panoramic radiographs.International journal of legal medicine · 2024Article
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Authors and funding
7 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDental development assessment is an important factor in dental age estimation and dental maturity evaluation. This study aimed to develop and evaluate the performance of an automated dental development staging system based on Demirjian's method using deep learning.
methodsThe study included 5133 anonymous panoramic radiographs obtained from the Department of Pediatric Dentistry database at Seoul National University Dental Hospital between 2020 and 2021. The proposed methodology involves a three-step procedure for dental staging: detection, segmentation, and classification. The panoramic data were randomly divided into training and validating sets (8:2), and YOLOv5, U-Net, and EfficientNet were trained and employed for each stage. The models' performance, along with the Grad-CAM analysis of EfficientNet, was evaluated.
resultsThe mean average precision (mAP) was 0.995 for detection, and the segmentation achieved an accuracy of 0.978. The classification performance showed F1 scores of 69.23, 80.67, 84.97, and 90.81 for the Incisor, Canine, Premolar, and Molar models, respectively. In the Grad-CAM analysis, the classification model focused on the apical portion of the developing tooth, a crucial feature for staging according to Demirjian's method.
conclusionsThese results indicate that the proposed deep learning approach for automated dental staging can serve as a supportive tool for dentists, facilitating rapid and objective dental age estimation and dental maturity evaluation.
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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.