A Portable <scp>AI</scp> ‐Enabled Intraoral Imaging System for Real‐Time Detection and Staging of Dental Caries

ABSTRACT Dental caries is one of the most common oral diseases and it requires advanced imaging techniques for early and accurate detection. This paper presents a portable AI‐enabled intraoral imaging system inspired by the YOLOv8 deep learning model and equipped with a low‐cost hardware Raspberry Pi 4 and intraoral endoscopy camera. Intraoral photos that were clinically obtained were annotated as per the American Dental Association ADA criteria of caries and utilized in the training and validation of the system. The optimized model achieved 81.5% accuracy, with precision, recall, and F1‐scores of 82%, 81%, and 81%, respectively, for stage‐specific detection of caries. Real‐time performance and low computational overhead were demonstrated, highlighting the feasibility of deploying embedded AI for point‐of‐care imaging. This hardware–software co‐designed system introduces a cost‐effective and scalable diagnostic platform for intraoral applications, and its framework is extendable to other medical imaging tasks requiring portable, AI‐driven solutions.