Artificial Intelligence for Otosclerosis Detection: A Pilot Study.

Abstract

The gold standard for otosclerosis diagnosis, aside from surgery, is high-resolution temporal bone computed tomography (TBCT), but it can be compromised by the small size of the lesions. Many artificial intelligence (AI) algorithms exist, but they are not yet used in daily practice for otosclerosis diagnosis. The aim was to evaluate the diagnostic performance of AI in the detection of otosclerosis. This case-control study included patients with otosclerosis surgically confirmed (2010-2020) and control patients who underwent TBCT and for whom radiological data were available. The AI algorithm interpreted the TBCT to assign a positive or negative diagnosis of otosclerosis. A double-blind reading was then performed by two trained radiologists, and the diagnostic performances were compared according to the best combination of sensitivity and specificity (Youden index). A total of 274 TBCT were included (174 TBCT cases and 100 TBCT controls). For the AI algorithm, the best combination of sensitivity and specificity was 79% and 98%, with an ideal diagnostic probability value estimated by the Youden index at 59%. For radiological analysis, sensitivity was 84% and specificity 98%. The diagnostic performance of the AI algorithm was comparable to that of a trained radiologist, although the sensitivity at the estimated ideal threshold was lower.