Empowering Portable Optoelectronics With Computer Vision for Intraoral Cavity Detection

Tooth decay is a chronic disease resulting in pain, infection, and tooth loss. This illness is common because many factors, such as poor oral hygiene, sugar consumption, and microbial flora, contribute to dental cavities. Untreated or undetected tooth decay often escalates to a more severe stage, emphasizing the importance of early detection and intervention. We propose a portable, low-cost, and ergonomic optoelectronic device to provide a possible solution for the early detection of dental cavities before annual or regular dental checkups for the first time. This device integrates mini cameras on top of a dental impression tray to capture images of the teeth, and the photographs can be transmitted via Wi-Fi to the cloud for real-time cavity detection through a you only look once (YOLO) algorithm that is based on a convolutional neural network (CNN). Our results show the precision, recall, and mean average precision (mAP)@0.5:0.95 for YOLOv5 (0.72, 0.70, 0.75), YOLOv6 (0.59, 0.50, 0.58), and YOLOv7 (0.93, 0.94, 0.82). We also compared the YOLO algorithm with traditional techniques such as support vector machine (SVM) and k-nearest neighbor (kNN) algorithms. This intraoral cavity detection system paves the way for early detection of dental cavities with quick accessibility and affordable cost. We foresee that this optoelectronic device will play a role in advancing biomedical technologies, ultimately promoting the long-term well-being of individuals.