ArticleDento maxillo facial radiology2022
Automated detection of dental restorations using deep learning on panoramic radiographs.
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 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Artificial Intelligence in Dental Treatment Planning and Diagnostic Decision-Making: A Systematic Review and Meta-Analysis.Clinical and experimental dental research · 2026Pooled it
- Deep Learning-Based Automated Diagnostic Charting on Panoramic Radiography: Comparison of YOLOv11 and YOLOv12.Odontology · 2026Article
- Deep learning-enhanced detection of dental restorations and orthodontic appliances in panoramic radiographs using a clinically annotated dataset.Acta odontologica Scandinavica · 2026Article
- Artificial Intelligence-Assisted Sex Estimation from Canine Measurements on Panoramic Radiographs: Comparison with Manual Analysis in a Romanian Sample.Diagnostics (Basel, Switzerland) · 2026Article
- Comparative performance of one-stage and two-stage deep learning models for instance segmentation of overhanging dental restorations on bitewing radiographs.Scientific reports · 2026Article
- Deep learning model development and clinical validation for radiographic surrogate markers of implant esthetic risk.NPJ digital medicine · 2026Article
- Deep Learning-Based Classification of Zirconia and Metal-Supported Porcelain Fixed Restorations on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Effect of AI-powered patient management systems in dental clinics.Bioinformation · 2026Article
- AI-Based Intraoral Videography for Automated Dental Inspection and Charting in Children With Mixed Dentition.International journal of telemedicine and applications · 2026Article
- The Influence of Examiner Experience on Inter- and Intra-Rater Agreement in Dental Restorations Identification on Three-Dimensional Digital Models.Medicina (Kaunas, Lithuania) · 2025Article
- In Silico possibilities to understand peri-implant bone healing- state of the Art.International journal of implant dentistry · 2025Review
- Performance of a Vision-Language Model in Detecting Common Dental Conditions on Panoramic Radiographs Using Different Tooth Numbering Systems.Diagnostics (Basel, Switzerland) · 2025Article
- Can super resolution via deep learning improve classification accuracy in dental radiography?Dento maxillo facial radiology · 2025Article
- Evaluating the Accuracy, Reliability, Consistency, and Readability of Different Large Language Models in Restorative Dentistry.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2025Article
- Segmentation of Pulp and Pulp Stones with Automatic Deep Learning in Panoramic Radiographs: An Artificial Intelligence Study.Dentistry journal · 2025Article
- Automatic detection of developmental stages of molar teeth with deep learning.BMC oral health · 2025Article
- Segmentation of periapical lesions with automatic deep learning on panoramic radiographs: an artificial intelligence study.BMC oral health · 2024Article
- Improving resolution of panoramic radiographs: super-resolution concept.Dento maxillo facial radiology · 2024Article
- STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning.Diagnostics (Basel, Switzerland) · 2024Article
- A YOLO-V5 approach for the evaluation of normal fillings and overhanging fillings: an artificial intelligence study.Brazilian oral research · 2024Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAutomatically detecting dental conditions using Artificial intelligence (AI) and reporting it visually are now a need for treatment planning and dental health management. This work presents a comprehensive computer-aided detection system to detect dental restorations.
methodsThe state-of-art ten different deep-learning detection models were used including R-CNN, Faster R-CNN, SSD, YOLOv3, and RetinaNet as detectors. ResNet-50, ResNet-101, XCeption-101, VGG16, and DarkNet53 were integrated as backbone and feature extractor in addition to efficient approaches such Side-Aware Boundary Localization, cascaded structures and simple model frameworks like Libra and Dynamic.Total 684 objects in panoramic radiographs were used to detect available three classes, namely, dental restorations, denture and implant.Each model was evaluated by mean average precision (mAP), average recall (AR), and precision-recall curve using Common Objects in Context (COCO) detection evaluation metrics.
resultsmAP varied between 0.755 and 0.973 for ten models explored while AR ranges between 0.605 and 0.771. Faster R-CNN RegnetX provided the best detection performance with mAP of 0.973 and AR of 0.771. Area under precision-recall curve was 0.952. Precision-recall curve indicated that errors were mainly dominated by localization confusions.
conclusionsResults showed that the proposed AI-based computer-aided system had great potential with reliable, accurate performance detecting dental restorations, denture and implant in panoramic radiographs. As training models with more data and standardization in reporting, AI-based solutions will be implemented to dental clinics for daily use soon.
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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.