ArticleScientific reports2025
Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8.
Article in Scientific reports, 2025. 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 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence in Dental Treatment Planning and Diagnostic Decision-Making: A Systematic Review and Meta-Analysis.Clinical and experimental dental research · 2026Pooled it
- Advancements in Caries Diagnostics Using Bitewing Radiography: A Systematic Review of Deep Learning Approaches.Caries research · 2026Pooled it
- Deep learning versus general dentists: a clinical evaluation of caries diagnostic accuracy on bitewing radiographs.BMC oral health · 2026Article
- Enhancing YOLOv8s with MHSA and GA for robust caries detection on periapical radiographs.BMC oral health · 2026Article
- EnamelNet-TRiX: A Lesion-Aware Dual-Transformer With Cross-Attention for Early and Advanced Enamel Caries Diagnosis.International dental journal · 2026Article
- Deep Learning-Based Dental Caries Diagnosis on Panoramic Radiographies: Performance of YOLOv8 Versus Human Observers.Diagnostics (Basel, Switzerland) · 2026Article
- OralSegNet: An Approach to Early Detection of Oral Disease Using Transfer Learning.Oral diseases · 2026Article
- Digital Dentistry in Clinical Practice: A Scoping Review of Current Capabilities and Future Directions.International dental journal · 2026Article
- Segmentation-Based Multi-Class Detection and Radiographic Charting of Periodontal and Restorative Conditions on Bitewing Radiographs Using Deep Learning.Diagnostics (Basel, Switzerland) · 2026Article
- A novel Pulp Caries GAN multi loss GAN with new pulp inspired metaheuristics for pediatric dental caries detection and segmentation.Scientific reports · 2026Article
- Integrating Numerical Data with AI-Based Image Processing Techniques to Improve the Diagnostic Accuracy of Detecting Dental Caries in Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Modular Deep-Learning Pipelines for Dental Caries Data Streams: A Twin-Cohort Proof-of-Concept.Dentistry journal · 2025Article
- [An lightweight algorithm for multi-dimensional optimization of intelligent detection of dental abnormalities on panoramic oral X-ray images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Artificial intelligence-assisted identification of condensing osteitis and idiopathic osteosclerosis on panoramic radiographs.Scientific reports · 2025Article
- Preliminary approach to creation of a prediction model for diagnosis of Sjögren's syndrome using radiomics and machine learning techniques on computed tomography images of the parotid glands.Imaging science in dentistry · 2025Article
- Closing Editorial: Advancements in Artificial Intelligence for Dentomaxillofacial Radiology-Current Trends and Future Directions.Diagnostics (Basel, Switzerland) · 2025Article
- Automated Classification of Dental Caries in Bitewing Radiographs Using Machine Learning and the ICCMS Framework.International journal of dentistry · 2025Article
- Artificial Intelligence Integrated with Intraoral Digital Imaging in Dental Caries Detection, Treatment Planning, and Clinical Decision-Making: A Scoping Review.F1000Research · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dental caries is a very common chronic disease that may lead to pain, infection, and tooth loss if its diagnosis at an early stage remains undetected. Traditional methods of tactile-visual examination and bitewing radiography, are subject to intrinsic variability due to factors such as examiner experience and image quality. This variability can result in inconsistent diagnoses. Thus, the present study aimed to develop a deep learning-based AI model using the YOLOv8 algorithm for improving interproximal caries detection in bitewing radiographs. In this retrospective study on 552 radiographs, a total of 1,506 images annotated at Tehran University of Medical Science were processed. The YOLOv8 model was trained and the results were evaluated in terms of precision, recall, and the F1 score, whereby it resulted in a precision of 96.03% for enamel caries and 80.06% for dentin caries, thus showing an overall precision of 84.83%, a recall of 79.77%, and an F1 score of 82.22%. This proves its reliability in reducing false negatives and improving diagnostic accuracy. YOLOv8 enhances interproximal caries detection, offering a reliable tool for dental professionals to improve diagnostic accuracy and clinical outcomes.
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