The Influence of a Deep Learning Tool on the Performance of Oral and Maxillofacial Radiologists in the Detection of Apical Radiolucencies

• Published in: Dento-maxillo-facial radiology 2024-12
• Main Authors: Hamdan, Manal, Uribe, Sergio E, Tuzova, Lyudmila, Tuzoff, Dmitry, Badr, Zaid, Mol, André, Tyndall, Donald A
• Format: Article
• Language: English
• ISSN: 0250-832X; 1476-542X; 1476-542X
• DOI: 10.1093/dmfr/twae054

The Influence of a Deep Learning Tool on the Performance of Oral and Maxillofacial Radiologists in the Detection of Apical Radiolucencies. This study aimed to assess the impact of a deep learning model on oral radiologists' ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effects of years of experience, time to diagnose, and specialty. This study used an annotated dataset and a beta-version of a deep learning model (Denti.AI). The testing subset comprised 68 intraoral periapical radiographs confirmed with cone-beam computed tomography for presence/absence of apical radiolucencies. Four oral radiologists participated in a crossover reading scenario, analyzing the radiographs under two conditions: initially without AI assistance and later with AI predictions. The study evaluated reader performance using AFROC-AUC, sensitivity, specificity, and ROC-AUC per case. It also assessed sensitivity per lesion. Regression analysis investigated how experience, time spent on images, and specialty influenced reader performance. No statistically significant differences were found in AFROC-AUC, sensitivity, specificity, and ROC-AUC. Regression analysis identified factors influencing diagnostic outcomes: unaided reading significantly prolonged diagnostic time (Beta = 12, 95% CI [11, 13], p