Artificial Intelligence in the Diagnosis of Cholesteatoma: A Systematic Review of Current Evidence

This systematic review evaluated the application of artificial intelligence (AI) in the imaging-based diagnosis of cholesteatoma. A comprehensive search of PubMed, Scopus, and Web of Science up to September 2025 identified 8,160 records, of which 7 studies met the inclusion criteria. Five studies used temporal bone CT, one investigated otoscopic images, and one assessed CT-based staging of mastoid extension. Reported internal validation accuracies were generally high (>90%), particularly with convolutional neural network (CNN) architectures, while external validations showed moderate performance (78-88%). A multicenter 3D convolutional neural network (CNN) with automated region-of-interest detection demonstrated consistent generalizability and aided surgical planning in prospective testing. The otoscopy-based model achieved high accuracy for differentiating cholesteatoma from normal membranes (>98%), though performance decreased when distinguishing it from other middle ear pathologies. Across studies, AI models often performed comparably to human readers, and explainability tools highlighted relevant diagnostic features. Most studies were retrospective and single-center, with limited external validation. Overall, AI shows strong potential for cholesteatoma diagnosis using CT and otoscopic imaging, but multicenter prospective studies with standardized evaluation and clinical impact assessment are needed before routine implementation.