Genset Radiology: Deep Learning Approaches for Periapical Lesion Detection on IOPA

Abstract

Artificial intelligence (AI) has transformed diagnostic radiology by introducing automation, precision, and reproducibility. Intraoral periapical (IOPA) radiographs remain indispensable for detecting periapical lesions, yet interpretation accuracy is often limited by observer subjectivity and image quality. AI-driven algorithms, especially deep learning architectures, have demonstrated significant promise in identifying and classifying periapical pathologies. This narrative review synthesizes evidence from recent literature on the applications of AI in the detection and diagnosis of periapical lesions using IOPA radiographs. Studies indicate that convolutional neural networks (CNNs) achieve diagnostic performance comparable to trained radiologists, improving early detection and reducing diagnostic errors. The review discusses various AI models, their clinical relevance, limitations, and future implications for oral medicine and radiology. Despite advancements, challenges related to data diversity, algorithm transparency, and ethical compliance persist. The integration of AI into dental diagnostics marks a paradigm shift toward precision imaging and augmented decision-making in oral healthcare.