4.7 Article

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

期刊

JOURNAL OF PERSONALIZED MEDICINE
卷 12, 期 11, 页码 -

出版社

MDPI
DOI: 10.3390/jpm12111855

关键词

machine learning; tympanic membrane; middle ear disease; diagnosis; accuracy

资金

  1. Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) - theMinistry of Health & Welfare, Republic of Korea [HI21C1574]
  2. National Research Foundation of Korea (NRF) - Korea government [2021R1F1A1054810]
  3. National Research Foundation of Korea [2021R1F1A1054810] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

Teachable Machine (R) is a machine learning platform that can diagnose tympanic membrane lesions without coding knowledge. It achieved high accuracy in classifying normal and abnormal tympanic membranes, but the accuracy decreased with the complexity of pathology categorization.
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.

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