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

Authors

• Manal Hamdan, Marquette UniversityFollow
• Sergio E. Uribe, Riga Stradins University
• Lyudmila Tuzova, Georgia Institute of Technology
• Dmitry Tuzoff, Denti.Ai Technology Inc.
• Zaid Badr, Marquette UniversityFollow
• Andre Mol, UNC Adams School of Dentistry
• Donald A. Tyndall, UNC Adams School of Dentistry

Document Type

Article

Publication Date

2-2025

Publisher

British Institute of Radiology

Source Publication

Dentomaxillofacial Radiology (DMFR)

Source ISSN

0250-832X

Original Item ID

DOI: 10.1093/dmfr/twae054

Abstract

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.

Comments

Dentomaxillofacial Radiology (DMFR), Vol. 54, No. 2 (February 2025): 118-124. DOI.

Recommended Citation

Hamdan, Manal; Uribe, Sergio E.; Tuzova, Lyudmila; Tuzoff, Dmitry; Badr, Zaid; Mol, Andre; and Tyndall, Donald A., "The Influence of a Deep Learning Tool on the Performance of Oral and Maxillofacial Radiologists in the Detection of Apical Radiolucencies" (2025). School of Dentistry Faculty Research and Publications. 608.
https://epublications.marquette.edu/dentistry_fac/608