ArticleDento maxillo facial radiology2022
Deep learning for automated detection and numbering of permanent teeth on panoramic images.
Article in Dento maxillo facial radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 5 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review.International dental journal · 2024Pooled it
- Panoramic imaging errors in machine learning model development: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review.BMC oral health · 2024Pooled it
- Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis.Dento maxillo facial radiology · 2024Pooled it
- Development of an automated YOLO v8-based deep learning teeth numbering model for digital orthopantomogram.Oral radiology · 2026Article
- Evaluating the Artificial Intelligence Driven Monitoring System Notifications: Clear Aligner and Oral Hygiene Perspectives.Orthodontics & craniofacial research · 2026Article
- Demographic prompt cues shift clinical recommendations in multimodal large language models: a multi-model audit of 25,380 dental radiograph assessments.BMC oral health · 2026Article
- Deep learning detection of ectopic canines and molars in mixed dentition.Scientific reports · 2026Article
- A Lightweight and Explainable AI Framework Toward Automated Infraocclusion Detection in Pediatric Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Application of Artificial Intelligence in Detecting Dental Anomalies: Current Models, Imaging Modalities, and Future Directions.Health science reports · 2026Article
- Automated surgical instrument recognition in laparoscopic cholecystectomy videos: a novel two-step deep learning approach with virtual image synthesis.Surgical endoscopy · 2026Article
- Artificial Intelligence in Pediatric Dentistry: A Systematic Review and Meta-Analysis.Children (Basel, Switzerland) · 2026Review
- AI-Based Intraoral Videography for Automated Dental Inspection and Charting in Children With Mixed Dentition.International journal of telemedicine and applications · 2026Article
- How artificial intelligence is reshaping citation impact in oral and maxillofacial radiology journals: an 8-year analysis with editorial and clinical implications (JCR 2017-2024).Frontiers in research metrics and analytics · 2026Article
- Application of artificial intelligence in oral health management: challenges and opportunities.Frontiers in medicine · 2026Review
- Age Estimation of the Cervical Vertebrae Region Using Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- AI-powered precision in dental radiographic analysis using tailored CNNs for tooth numbering and cavity detection.PLOS digital health · 2025Article
- Determination of Kennedy's classification in panoramic X-rays by automated tooth labeling.International journal of computer assisted radiology and surgery · 2025Article
- Three-Dimensional Semantic Segmentation of Palatal Rugae and Maxillary Teeth and Motion Evaluation of Orthodontically Treated Teeth Using Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to evaluate an automated detection system to detect and classify permanent teeth on orthopantomogram (OPG) images using convolutional neural networks (CNNs).
methodsIn total, 591 digital OPGs were collected from patients older than 18 years. Three qualified dentists performed individual teeth labelling on images to generate the ground truth annotations. A three-step procedure, relying upon CNNs, was proposed for automated detection and classification of teeth. Firstly, U-Net, a type of CNN, performed preliminary segmentation of tooth regions or detecting regions of interest (ROIs) on panoramic images. Secondly, the Faster R-CNN, an advanced object detection architecture, identified each tooth within the ROI determined by the U-Net. Thirdly, VGG-16 architecture classified each tooth into 32 categories, and a tooth number was assigned. A total of 17,135 teeth cropped from 591 radiographs were used to train and validate the tooth detection and tooth numbering modules. 90% of OPG images were used for training, and the remaining 10% were used for validation. 10-folds cross-validation was performed for measuring the performance. The intersection over union (IoU), F1 score, precision, and recall (
resultsThe ROI detection module had an IoU of 0.70. The tooth detection module achieved a recall of 0.99 and a precision of 0.99. The tooth numbering module had a recall, precision and F1 score of 0.98.
conclusionThe resultant automated method achieved high performance for automated tooth detection and numbering from OPG images. Deep learning can be helpful in the automatic filing of dental charts in general dentistry and forensic medicine.
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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.