{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T12:28:40Z","timestamp":1791548920953,"version":"4.3.4"},"reference-count":19,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T00:00:00Z","timestamp":1626825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF- 2018R1D1A1B07048796"],"award-info":[{"award-number":["NRF- 2018R1D1A1B07048796"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF- 2021R1F1A1054810"],"award-info":[{"award-number":["NRF- 2021R1F1A1054810"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>The present study aimed to develop a machine learning network to diagnose middle ear diseases with tympanic membrane images and to identify its assistive role in the diagnostic process. The medical records of subjects who underwent ear endoscopy tests were reviewed. From these records, 2272 diagnostic tympanic membranes images were appropriately labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), or cholesteatoma and were used for training. We developed the \u201cResNet18 + Shuffle\u201d network and validated the model performance. Seventy-one representative cases were selected to test the final accuracy of the network and resident physicians. We asked 10 resident physicians to make diagnoses from tympanic membrane images with and without the help of the machine learning network, and the change of the diagnostic performance of resident physicians with the aid of the answers from the machine learning network was assessed. The devised network showed a highest accuracy of 97.18%. A five-fold validation showed that the network successfully diagnosed ear diseases with an accuracy greater than 93%. All resident physicians were able to diagnose middle ear diseases more accurately with the help of the machine learning network. The increase in diagnostic accuracy was up to 18% (1.4% to 18.4%). The machine learning network successfully classified middle ear diseases and was assistive to clinicians in the interpretation of tympanic membrane images.<\/jats:p>","DOI":"10.3390\/jcm10153198","type":"journal-article","created":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T11:53:23Z","timestamp":1626868403000},"page":"3198","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["An Assistive Role of a Machine Learning Network in Diagnosis of Middle Ear Diseases"],"prefix":"10.3390","volume":"10","author":[{"given":"Hayoung","family":"Byun","sequence":"first","affiliation":[{"name":"Department of Otolaryngology & Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea"},{"name":"Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1773-1278","authenticated-orcid":false,"given":"Sangjoon","family":"Yu","sequence":"additional","affiliation":[{"name":"Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea"},{"name":"Department of Computer Science, Hanyang University, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8055-1467","authenticated-orcid":false,"given":"Jaehoon","family":"Oh","sequence":"additional","affiliation":[{"name":"Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea"},{"name":"Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8408-0540","authenticated-orcid":false,"given":"Junwon","family":"Bae","sequence":"additional","affiliation":[{"name":"Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea"},{"name":"Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1019-3347","authenticated-orcid":false,"given":"Myeong Seong","family":"Yoon","sequence":"additional","affiliation":[{"name":"Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea"},{"name":"Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2001-7689","authenticated-orcid":false,"given":"Seung Hwan","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology & Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6884-7927","authenticated-orcid":false,"given":"Jae Ho","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology & Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea"},{"name":"Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea"},{"name":"Department of HY-KIST Bio-Convergence, College of Medicine, Hanyang University, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tae Hyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea"},{"name":"Department of Computer Science, Hanyang University, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.amjmed.2017.10.035","article-title":"Artificial Intelligence in Medical Practice: The Question to the Answer?","volume":"131","author":"Miller","year":"2018","journal-title":"Am. 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