ArticleCaries research2025
Deep Learning-Based Algorithm for Staging Secondary Caries in Bitewings.
Article in Caries research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in Caries Diagnostics Using Bitewing Radiography: A Systematic Review of Deep Learning Approaches.Caries research · 2026Pooled it
- Enhanced dental caries detection using ConvNeXt-FPN with Faster R-CNN framework on panoramic and intraoral images.Imaging science in dentistry · 2026Article
- Enhancing YOLOv8s with MHSA and GA for robust caries detection on periapical radiographs.BMC oral health · 2026Article
- Deep Learning-Based Dental Caries Diagnosis: A Modality-Stratified Systematic Review and Meta-Analysis of Faster R-CNN and Mask R-CNN.Diagnostics (Basel, Switzerland) · 2026Review
- Segmentation-Based Multi-Class Detection and Radiographic Charting of Periodontal and Restorative Conditions on Bitewing Radiographs Using Deep Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice.Frontiers in medicine · 2026Review
- From remote screening to precision prevention: responsible multimodal AI for risk prediction and equitable oral healthcare.Frontiers in oral health · 2026Review
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDespite the notable progress in developing artificial intelligence-based tools for caries detection in bitewings, limited research has addressed the detection and staging of secondary caries. Therefore, we aimed to develop a convolutional neural network (CNN)-based algorithm for these purposes using a novel approach for determining lesion severity.
methodsWe used a dataset from a Dutch dental practice-based research network containing 2,612 restored teeth in 413 bitewings from 383 patients aged 15-88 years and trained the Mask R-CNN architecture with a Swin Transformer backbone. Two-stage training fine-tuned caries detection accuracy and severity assessment. Annotations of caries around restorations were made by two evaluators and checked by two other experts. Aggregated accuracy metrics (mean ± standard deviation - SD) in detecting teeth with secondary caries were calculated considering two thresholds: detecting all lesions and dentine lesions. The correlation between the lesion severity scores obtained with the algorithm and the annotators' consensus was determined using the Pearson correlation coefficient and Bland-Altman plots.
resultsOur refined algorithm showed high specificity in detecting all lesions (0.966 ± 0.025) and dentine lesions (0.964 ± 0.019). Sensitivity values were lower: 0.737 ± 0.079 for all lesions and 0.808 ± 0.083 for dentine lesions. The areas under ROC curves (SD) were 0.940 (0.025) for all lesions and 0.946 (0.023) for dentine lesions. The correlation coefficient for severity scores was 0.802.
conclusionWe developed an improved algorithm to support clinicians in detecting and staging secondary caries in bitewing, incorporating an innovative approach for annotation, considering the lesion severity as a continuous outcome.
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