Dental Caries Detection System Using Machine Learning and IOT

Dental caries, a widespread oral health issue, necessitates early and accurate detection to ensure effective treatment and prevention. Traditional diagnostic methods often involve manual inspection and X-ray imaging, which can be time-consuming and subject to human error. This study introduces a cutting-edge approach by integrating machine learning with the YOLO (You Only Look Once) algorithm and leveraging the computational power of a Raspberry Pi to automate and enhance caries detection. The research focuses on developing a real-time, portable system for detecting dental caries from intraoral images using a Raspberry Pi. The YOLO algorithm, known for its efficiency in real-time object detection, is employed to train a model capable of identifying and classifying carious lesions in dental radiographs with high precision. This study aims to advance dental diagnostic capabilities by combining machine learning and embedded systems technology. The use of the YOLO algorithm with Raspberry Pi not only enhances the accuracy and speed of caries detection but also makes this advanced technology accessible and practical for everyday clinical use.