Integrating AI-Based Radiographic Analysis for Enhanced Detection of Early Carious Lesions in Dental Practice: A Pragmatic Approach

Abstract

The early detection of carious lesions is paramount in dentistry, as it drastically improves treatment outcomes and patient satisfaction. This study investigates the efficacy of artificial intelligence (AI)-driven radiographic analysis in the identification of incipient caries. Using a dataset comprising 5,000 dental radiographs, a convolutional neural network (CNN) was trained and validated. The model achieved a sensitivity of 92% and a specificity of 87%, significantly outperforming traditional diagnostic methods. Our findings indicate that the implementation of AI enhances clinical decision-making processes by providing real-time diagnostics that are both reliable and cost-effective. The integration of such technology could revolutionize practices, reduce the burden on dental professionals, and ultimately facilitate superior patient care.