Detection of Cholesteatoma Residues in Surgical Videos Using Artificial Intelligence

Surgical treatment is the only option for cholesteatoma; however, the recurrence rate is high, and the incidence of residual cholesteatoma recurrence largely depends on the surgeon's skill. Training deep neural network (DNN) models typically requires large datasets, but the prevalence of cholesteatoma is low (1 in 25,000 people). However, cholesteatoma remains difficult to treat. Developing analytical methods to improve ac-curacy with limited datasets remains a significant challenge in medical artificial intelli-gence (AI) research. This study introduces an AI-based system for detecting residual cholesteatoma in surgical field videos. A retrospective analysis was conducted on 144 cases from 88 patients who underwent surgery. The training dataset comprised videos of cholesteatoma lesions recorded during surgery and intact middle ear mucosa after lesion removal. These videos were captured using both endoscope and microscope for AI model development. The diagnostic accuracy was approximately 80% for both endoscopic and microscopic images. Although the diagnostic accuracy for microscopic images was slightly lower, focusing on the lesion center improved the accuracy to a level comparable to that of endoscopic images. This study demonstrates the diagnostic feasibility of AI-based cholesteatoma detection despite a limited sample size highlighting the value of proof-of-concept studies in clar-ifying technical requirements for future clinical systems and is the first AI study to use videos from both modalities.