A Mobile Application for the Detection of Pre-Carious Lesions in Peruvian Patients based on YOLOv7

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 application achieved an average accuracy of 94%, with both accuracy and Positive Predictive Value (PPV) exceeding 90% in most cases. The results demonstrated consistent diagnostic accuracy and efficiency, validating the application's performance. Patient surveys reflected high satisfaction, with average scores of 4.4 for usability, 4.2 for efficiency, and 4.6 for functionality. Similarly, dentists rated the usability, functionality, and efficiency of the application with average scores of 4.5. These findings highlight the potential of the application to improve clinical workflows and accuracy in detecting early carious lesions.