ArticleBMC medical imaging2024
YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 papers.
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25 citing papers in PubMed.
- Development of an automated YOLO v8-based deep learning teeth numbering model for digital orthopantomogram.Oral radiology · 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
- 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
- Automated detection of periapical lesions in pediatric panoramic radiographs using YOLOv7: a retrospective internal validation study.BMC pediatrics · 2026Article
- Investigation of the Use of Artificial Intelligence in Anterior Loop Detection: A Panoramic Radiography Study.Diagnostics (Basel, Switzerland) · 2026Article
- Automated periodontitis diagnosis and staging using an end-to-end deep learning model on panoramic dental radiographs.Oral radiology · 2026Article
- Deep‑learning‑based detection of open‑apex teeth on panoramic radiographs using YOLO models.Oral radiology · 2026Article
- Structure-Guided Tooth Numbering and Lesion Localization in Visible Light Oral Images.Journal of imaging · 2026Article
- Robust oriented object detection for posterior teeth in mixed dentition.Scientific reports · 2026Article
- AKUDENTAL teeth instance segmentation dataset: a cross-dataset analysis.BMC oral health · 2026Article
- A novel Pulp Caries GAN multi loss GAN with new pulp inspired metaheuristics for pediatric dental caries detection and segmentation.Scientific reports · 2026Article
- Challenges and Limitations of Multimodal Large Language Models in Interpreting Pediatric Panoramic Radiographs.International journal of paediatric dentistry · 2026Article
- Article
- AI-Based Intraoral Videography for Automated Dental Inspection and Charting in Children With Mixed Dentition.International journal of telemedicine and applications · 2026Article
- Using swin UNETR deep model for automated detection of alveolar bone fenestration/dehiscence in CBCT.Frontiers in bioengineering and biotechnology · 2026Article
- Automatic recognition of oral health status in mixed dentition via intraoral photography.BMC oral health · 2025Article
- Artificial Intelligence-Aided Tooth Detection and Segmentation on Pediatric Panoramic Radiographs in Mixed Dentition Using a Transfer Learning Approach.Diagnostics (Basel, Switzerland) · 2025Article
- U-Net-Based Deep Learning for Simultaneous Segmentation and Agenesis Detection of Primary and Permanent Teeth in Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning approach for tooth numbering and restoration detection on pediatric periapical radiographs in mixed dentition.Clinical oral investigations · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesIn the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs.
methodsA total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed.
resultsThe sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation.
conclusionsYOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.
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