Journal Article

Deep Learning Applications for Dental-Disease Classification Using Intraoral Photographic Images: Current Status and Future Perspectives

Thirupathy, Karthiga, Altarakemah, Yacoub Yousef, Mutawa, A. M.

2026

Overview

Dental conditions, including caries, periodontal disease, plaque accumulation, malocclusion, and oral mucosal abnormalities, remain highly prevalent worldwide. Early detection is crucial for preventing disease progression, simplifying treatment, and improving patient outcomes. Conventional diagnostic methods rely on subjective visual and tactile examinations, which are often inconsistent. Recent advances in deep learning (DL), particularly convolutional neural networks and vision transformers, enable automated, accurate detection of dental diseases from intraoral images captured via smartphones or dedicated imaging devices. DL-driven systems facilitate cost-effective virtual consultations, community screenings, and remote oral health monitoring. This narrative review was conducted following a structured search of PubMed, Scopus, Web of Science, Embase, and Google Scholar (October 2020–October 2025), which identified 74 eligible studies on intraoral photographic imaging-based DL systems, encompassing caries, gingival inflammation, plaque, malocclusion, and soft-tissue lesions. Most studies focused on caries, plaque, and periodontal disease using CNN and U-Net-based models, often reporting accuracies above 85% but with substantial performance drops in external validation. Despite promising results, clinical integration remains limited by challenges such as class imbalance, limited external validation, heterogeneous imaging protocols, and insufficient model interpretability. Emerging approaches, including self-supervised and federated learning, explainable artificial intelligence, multimodal data fusion, and smartphone-based diagnostics, offer potential solutions. Standardized imaging workflows, high-quality annotations, and robust clinical trials are essential to translate DL-based dental diagnostic systems into real-world practice. This narrative review aims to guide the development of reliable, equitable, and clinically deployable DL solutions for oral health assessment.

• Publisher: MDPI AG
• Subject: Abnormalities / Accuracy / Annotations / Artificial intelligence / Artificial neural networks / Bias / Business metrics / convolutional neural networks / Data integration / Datasets / Deep learning / Dental caries / Dental materials / Dentistry / Diagnostic systems / Disease / Explainable artificial intelligence / Federated learning / intraoral photographic images / Machine learning / Medical imaging / Neural networks / oral diseases / Periodontal disease / Photography / Privacy / Remote monitoring / Smartphones / transfer learning / Vision transformers