Web-Based Dental Disease Detection Using the YOLOv8 Algorithm

Authors

Rian Purnama Universitas Kebangsaan Republik Indonesia
Iim Abdurrohim Universitas Kebangsaan Republik Indonesia
Irman Hariman Universitas Kebangsaan Republik Indonesia
Deni Suprihadi Universitas Kebangsaan Republik Indonesia

Keywords:

YOLO, Dental Disease, Web-based System, Deep Learning, Tanggeung Health Center

Abstract

Oral health remains a significant issue in Indonesia, including at the Tanggeung Community Health Center, where limited equipment often leads to slow and subjective manual diagnosis. This study aims to develop a web-based dental disease detection system using the YOLOv8 algorithm to improve screening efficiency. The system uses a microservices architecture with a React.js frontend, FastAPI backend, and a detection model trained on 5,240 radiographic images. Test results show the model can detect five diseases (Calculus, Caries, Abscess, Gingivitis, Plaque) with an average accuracy (mAP) of 87.2% and an inference time of 45 milliseconds. User trials yielded a 90% usability rating, confirming the system's potential as a fast and accurate diagnostic aid in resource-limited areas.

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