Integrating Deep Learning Algorithms for Enhanced Detection of Periapical Pathologies in Endodontic Imaging

Abstract

The integration of deep learning algorithms into endodontic imaging represents a transformative advancement in diagnostic dentistry. This study focuses on enhancing the detection accuracy of periapical pathologies using convolutional neural networks (CNNs) trained on digital radiographs. By employing automated image preprocessing and feature extraction, the system identifies periapical lesions with higher precision compared to conventional radiographic interpretation. The research further evaluates model performance through metrics such as accuracy, sensitivity, and specificity, aligning results with expert diagnostic assessments. Findings demonstrate that deep learning-based systems significantly improve diagnostic efficiency, reduce observer variability, and offer real-time decision support in clinical endodontics. This integration not only accelerates diagnostic workflows but also paves the way for more reliable, data-driven clinical interventions in dental radiography.