Dental cavities represent a significant global health challenge, particularly in low-and middle-income countries, where early detection and diagnosis can substantially improve clinical outcomes. This study presents the development of a mobile application that utilizes YOLOv7 to detect early carious lesions on intraoral images, intending to provide dental professionals with a tool for timely diagnosis and intervention. The research was carried out in three key phases: analysis of YOLOv7, system development, and validation. The application was trained in a real clinical environment in Peru in collaboration with two independent dentists and their patients in two private clinics. Intraoral images were collected and processed from 40 participants, ensuring complete adherence to the ethical and privacy standards required for clinical studies. The experimental results demonstrated that the appl...

Tooth decay is a global challenge due to lack of dental care and excessive sugar consumption. These generate expensive dental treatments and affect quality of life, self-esteem, and productivity. Due to this, an approach is proposed for the detection of carious pre-lesions through dental image processing and using 2 Deep learning architectures most used in the literature: YOLOv7 and Faster RCNN. The approach is developed in 4 phases: (i) acquisition of the dataset, (ii) development of architectures, (iii) performance evaluation and (iv) analysis of results. Both architectures focus on the use of a public dataset composed of a total of 9,327 images of Intraoral Photographs classified into 3 classes: “teeth with cavities” (0), “teeth without cavities” (1) and “teeth with amalgam” (2). A web system was built with the model that had the best performance. The results showed that t...