A New Insight into Imaging Diagnosis of Otosclerosis Enhanced by Machine Learning and Radiomics

Otosclerosis is a disease affecting the middle and inner ear, characterized by abnormal bone remodeling that leads to stapes fixation and progressive hearing loss. Although high-resolution computed tomography (HRCT) is the standard imaging modality for diagnosis, its sensitivity is limited, with a high false-negative rate (FNR). This study investigates the use of radiomics and machine learning (ML) to improve diagnostic accuracy. HRCT scans from 99 subjects (48 otosclerosis, 51 controls) were analyzed, focusing on the stapes, antefenestral region (AF), and oval window (OW). From each scan, 6048 radiomic features were extracted and reduced to 1317 through feature selection. Statistical analyses and ML modeling were performed using the selected features. Sixty-seven biomarkers showed significant differences between cases and controls, primarily in the AF (56) and stapes (11); none were found in the OW. Both the AF and stapes exhibited increased heterogeneity in otosclerosis, reflecting the bone remodeling process. A reduction in the stapes' major axis was also observed, possibly related to torsional deformation. Image transformation filters enhanced disease visibility. Among several ML classifiers tested, L2-regularized logistic regression performed best, achieving an AUC of 0.90 ± 0.06, thereby enhancing the diagnostic accuracy reported in some studies for radiologists. Hierarchical clustering of the most predictive features further confirmed their strong discriminative power. Our findings highlight the potential of radiomics and ML to standardize otosclerosis diagnosis, reduce FNR, and support surgical decision-making. Future studies should validate these results using larger cohorts and advanced imaging technologies such as Photon-Counting CT.