ArticleClinical oral investigations2024
Automatic caries detection in bitewing radiographs-Part II: experimental comparison.
Article in Clinical oral investigations, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Student Performance in Bitewing Caries Detection: Artificial Intelligence Versus Alternative E-learning.International dental journal · 2026Article
- Enhancing YOLOv8s with MHSA and GA for robust caries detection on periapical radiographs.BMC oral health · 2026Article
- Deep Learning-Based Dental Caries Diagnosis on Panoramic Radiographies: Performance of YOLOv8 Versus Human Observers.Diagnostics (Basel, Switzerland) · 2026Article
- Automated deep learning detection of orthodontically induced external apical root resorption in maxillary incisors on panoramic radiographs.Progress in orthodontics · 2026Article
- Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice.Frontiers in medicine · 2026Review
- Accuracy of deep learning-based AI models for early caries lesion detection: the influence of annotation quality and reference choice.Clinical oral investigations · 2025Article
- Evaluation of Caries Detection on Bitewing Radiographs: A Comparative Analysis of the Improved Deep Learning Model and Dentist Performance.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2025Article
- Artificial intelligence enabled smart phone app for real-time caries detection on bitewing radiographs.Bioinformation · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveThe objective of this study was to compare the detection of caries in bitewing radiographs by multiple dentists with an automatic method and to evaluate the detection performance in the absence of a reliable ground truth. MATERIALS AND
methodsFour experts and three novices marked caries using bounding boxes in 100 bitewing radiographs. The same dataset was processed by an automatic object detection deep learning method. All annotators were compared in terms of the number of errors and intersection over union (IoU) using pairwise comparisons, with respect to the consensus standard, and with respect to the annotator of the training dataset of the automatic method.
resultsThe number of lesions marked by experts in 100 images varied between 241 and 425. Pairwise comparisons showed that the automatic method outperformed all dentists except the original annotator in the mean number of errors, while being among the best in terms of IoU. With respect to a consensus standard, the performance of the automatic method was best in terms of the number of errors and slightly below average in terms of IoU. Compared with the original annotator, the automatic method had the highest IoU and only one expert made fewer errors.
conclusionsThe automatic method consistently outperformed novices and performed as well as highly experienced dentists. CLINICAL SIGNIFICANCE: The consensus in caries detection between experts is low. An automatic method based on deep learning can improve both the accuracy and repeatability of caries detection, providing a useful second opinion even for very experienced dentists.
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