A Real-Time Edge-Based Deep Learning System For Automated Dental Cavity Detection And Risk Prediction Using Yolov5 And Raspberry Pi

Authors: Ashish Parekh, Devesh Tomar, Dhruv Selopal, Jenil Patel, Prof. Biju Balakrishnan

Abstract: Early detection of dental caries is essential for preventing severe oral complications, yet conventional diagnostic approaches rely heavily on manual visual examination and radiographic interpretation, which are often subjective and resource-intensive. This paper presents a real-time edge-based deep learning system for automated dental cavity detection and risk prediction using a Raspberry Pi-powered intraoral imaging platform. The proposed framework integrates a high-resolution camera module with a YOLOv5 object detection model trained on annotated dental image datasets. The system performs on-device inference, enabling real-time cavity localisation without reliance on cloud infrastructure. A confidence-based filtering mechanism reduces false positives and improves diagnostic reliability. A lightweight risk prediction module analyses historical detection patterns to assist in preventive dental assessment. Experimental validation demonstrates strong agreement between model predictions and expert annotations, confirming the system’s reliability and feasibility for deployment in resource-constrained environments.

DOI: https://doi.org/10.5281/zenodo.19760227