Objectives: 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.
Methods: 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 the presence/absence of apical radiolucencies. Four oral radiologists participated in a cross-over reading scenario, analysing the radiographs under 2 conditions: initially without AI assistance and later with AI predictions. The study evaluated reader performance using Alternative Free-Response Receiver Operating Characteristic - Area Under the Curve (AFROC-AUC), sensitivity, specificity, and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) per case. It also assessed sensitivity per lesion. Regression analysis investigated how experience, time spent on images, and specialty influenced reader performance.
Results: 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 (β = 12, 95% CI, 11-13, P < 0.001), while radiologists' professional status was positively associated with diagnostic accuracy (β = 0.02, 95% CI, 0.00-0.04, P = 0.015). These findings underscore the impact of AI on diagnostic efficiency and the critical role of radiologists' experience in diagnostic accuracy.
Conclusion: AI did not significantly enhance radiologists' overall diagnostic accuracy. However, it showed potential to enhance efficiency, particularly advantageous for non-expert clinicians. The expertise of radiologists remains vital for accuracy, underscoring the complementary role of AI in dental diagnostics.
Keywords: apical lesions; apical radiolucencies; artificial intelligence; computer vision; cone-beam computed tomography (CBCT); deep learning; dentistry; machine learning.
© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology and the International Association of Dentomaxillofacial Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.