ArticleDento maxillo facial radiology2024
Automatic deep learning detection of overhanging restorations in bitewing radiographs.
Article in Dento maxillo facial radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Periodontitis Risk Assessment and Prevention Planning: Comparative Study of Multimodal Large Language Models and Periodontist Evaluations.Journal of medical Internet research · 2026Article
- Radiographic assessment of the relationship between alveolar bone loss and overhanging margins of posterior dental restorations in diabetic patients: a retrospective study.Oral radiology · 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
- Diagnostic and localization performance of multimodal large language models in the interpretation of dental periapical radiographs.BMC oral health · 2026Article
- Deep learning model development and clinical validation for radiographic surrogate markers of implant esthetic risk.NPJ digital medicine · 2026Article
- Assessment of posterior interproximal surfaces on bitewing and panoramic radiographs by dental interns and an artificial intelligence application.Oral radiology · 2026Article
- Automated surgical instrument recognition in laparoscopic cholecystectomy videos: a novel two-step deep learning approach with virtual image synthesis.Surgical endoscopy · 2026Article
- Segmentation-Based Multi-Class Detection and Radiographic Charting of Periodontal and Restorative Conditions on Bitewing Radiographs Using Deep Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Challenges and Limitations of Multimodal Large Language Models in Interpreting Pediatric Panoramic Radiographs.International journal of paediatric dentistry · 2026Article
- An end-to-end deep-learning system for segmentation and classification of dental caries from radiovisiography images.Journal of conservative dentistry and endodontics · 2025Article
- Evaluation of the effect of overhanging restored teeth on alveolar bone by retrospective comparative fractal analysis.BMC oral health · 2025Article
- Artificial intelligence-assisted identification of condensing osteitis and idiopathic osteosclerosis on panoramic radiographs.Scientific reports · 2025Article
- The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry.International dental journal · 2025Review
- Accuracy of deep learning models in the detection of accessory ostium in coronal cone beam computed tomographic images.Scientific reports · 2025Article
- Machine learning models for prognosis prediction in regenerative endodontic procedures.BMC oral health · 2025Article
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7 authors.
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Abstract
objectivesThis study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms for the detecting and segmentation of overhanging dental restorations in bitewing radiographs.
methodsA total of 1160 anonymized bitewing radiographs were used to progress the artificial intelligence (AI) system for the detection and segmentation of overhanging restorations. The data were then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as You Only Look Once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed.
resultsThe model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the receiver operating characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87.
conclusionsThe findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures.
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