Caries detection using deep learning and convolutional neural networks from radiographic images: A narrative review

Sharifi, Farid and Ghadimi, Niloofar and Sharifi, Vahab and Anvarirad, Nadia (2025) Caries detection using deep learning and convolutional neural networks from radiographic images: A narrative review. World Journal of Biology Pharmacy and Health Sciences, 22 (2). pp. 359-368. ISSN 2582-5542

Dental caries remains one of the most prevalent chronic diseases worldwide, posing significant public health and economic burdens. Early and accurate diagnosis is critical for effective management and prevention of complications. While traditional diagnostic methods such as clinical examinations and radiographic assessments are widely used, they suffer from limitations including inter-observer variability, low sensitivity in early detection, and subjectivity. The emergence of artificial intelligence (AI), particularly deep learning through convolutional neural networks (CNNs), offers promising advancements in caries detection from dental radiographs. This narrative review explores the application of CNNs in diagnosing dental caries using various imaging modalities, including bitewing, panoramic, and periapical radiographs. We summarize current evidence from key studies employing architectures such as ResNet, VGGNet, U-Net, and EfficientNet, demonstrating superior diagnostic accuracy, sensitivity, and specificity when compared to conventional approaches. CNN-based models enhance objectivity, reduce diagnostic time, and offer scalable integration into clinical workflows. However, challenges remain regarding dataset standardization, overfitting, model generalizability, and the lack of interpretability of AI decisions. The review also highlights limitations in image quality, annotation variability, and regulatory constraints hindering clinical deployment. Future prospects include the adoption of explainable AI (XAI), multimodal data integration, and the development of optimized CNN architectures tailored for dental applications. These innovations could lead to more transparent, robust, and widely accepted diagnostic tools in dentistry. In conclusion, CNN-based caries detection represents a transformative shift in dental diagnostics, enhancing precision, efficiency, and accessibility. Addressing current limitations through technical, ethical, and regulatory advancements is essential to harness the full potential of AI-driven diagnostics and improve global oral health outcomes.