ArticleDiagnostics (Basel, Switzerland)2022
Detecting Proximal Caries on Periapical Radiographs Using Convolutional Neural Networks with Different Training Strategies on Small Datasets.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for binary dental caries diagnosis using intraoral images and dental radiographs: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- Diagnostic accuracy and repeatability of ChatGPT using textual and radiographic data in reversible pulpitis: a retrospective diagnostic study.Frontiers in bioinformatics · 2026Article
- Classification Algorithms for Early Tooth Demineralization Assessment by Impedance Spectroscopy.Sensors (Basel, Switzerland) · 2025Article
- Comparison of the Diagnostic Accuracy of an AI-Based System for Dental Caries Detection and Clinical Evaluation Conducted by Dentists.Journal of clinical medicine · 2025Article
- Detection of dental caries under fixed dental prostheses by analyzing digital panoramic radiographs with artificial intelligence algorithms based on deep learning methods.BMC oral health · 2025Article
- Artificial Intelligence Integrated with Intraoral Digital Imaging in Dental Caries Detection, Treatment Planning, and Clinical Decision-Making: A Scoping Review.F1000Research · 2025Article
- Design and implementation of a radiomic-driven intelligent dental hospital diversion system utilizing multilabel imaging data.Journal of translational medicine · 2024Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks' ensemble.Heliyon · 2024Article
- Deep Learning-Based Detection of Impacted Teeth on Panoramic Radiographs.Biomedical engineering and computational biology · 2024Article
- Diagnostic accuracy of dental caries detection using ensemble techniques in deep learning with intraoral camera images.PloS one · 2024Article
- Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging.Diagnostics (Basel, Switzerland) · 2023Article
- Review
- A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health.Diagnostics (Basel, Switzerland) · 2023Review
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5 authors.
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Abstract
The present study aimed to evaluate the performance of convolutional neural networks (CNNs) that were trained with small datasets using different strategies in the detection of proximal caries at different levels of severity on periapical radiographs. Small datasets containing 800 periapical radiographs were randomly categorized into a training and validation dataset (n = 600) and a test dataset (n = 200). A pretrained Cifar-10Net CNN was used in the present study. Different training strategies were used to train the CNN model independently; these strategies were defined as image recognition (IR), edge extraction (EE), and image segmentation (IS). Different metrics, such as sensitivity and area under the receiver operating characteristic curve (AUC), for the trained CNN and human observers were analysed to evaluate the performance in detecting proximal caries. IR, EE, and IS recognition modes and human eyes achieved AUCs of 0.805, 0.860, 0.549, and 0.767, respectively, with the EE recognition mode having the highest values (p all < 0.05). The EE recognition mode was significantly more sensitive in detecting both enamel and dentin caries than human eyes (p all < 0.05). The CNN trained with the EE strategy, the best performer in the present study, showed potential utility in detecting proximal caries on periapical radiographs when using small datasets.
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