Feasibility of the Machine Learning Network to Diagnose Tympanic Membrane Lesions without Coding Experience

Abstract

A machine learning platform operated without coding knowledge (Teachable machine (R)) has been introduced. The aims of the present study were to assess the performance of the Teachable machine (R) for diagnosing tympanic membrane lesions. A total of 3024 tympanic membrane images were used to train and validate the diagnostic performance of the network. Tympanic membrane images were labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), and cholesteatoma. According to the complexity of the categorization, Level I refers to normal versus abnormal tympanic membrane; Level II was defined as normal, OME, or COM + cholesteatoma; and Level III distinguishes between all four pathologies. In addition, eighty representative test images were used to assess the performance. Teachable machine (R) automatically creates a classification network and presents diagnostic performance when images are uploaded. The mean accuracy of the Teachable machine (R) for classifying tympanic membranes as normal or abnormal (Level I) was 90.1%. For Level II, the mean accuracy was 89.0% and for Level III it was 86.2%. The overall accuracy of the classification of the 80 representative tympanic membrane images was 78.75%, and the hit rates for normal, OME, COM, and cholesteatoma were 95.0%, 70.0%, 90.0%, and 60.0%, respectively. Teachable machine (R) could successfully generate the diagnostic network for classifying tympanic membrane.