{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T13:13:22Z","timestamp":1793452402823,"version":"4.5.0"},"reference-count":35,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T00:00:00Z","timestamp":1660608000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000092","name":"U.S. National Library of Medicine","doi-asserted-by":"publisher","award":["T15LM007092-30"],"award-info":[{"award-number":["T15LM007092-30"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aao-hnsfjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Otolaryngol.--head neck surg."],"published-print":{"date-parts":[[2023,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>We compared the diagnostic performance of human clinicians with that of a neural network algorithm developed using a library of tympanic membrane images derived from children taken to the operating room with the intent of performing myringotomy and possible tube placement for recurrent acute otitis media (AOM) or otitis media with effusion (OME).<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Design<\/jats:title><jats:p>Retrospective cohort study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Setting<\/jats:title><jats:p>Tertiary academic medical center from 2018 to 2021.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A training set of 639 images of tympanic membranes representing normal, OME, and AOM was used to train a neural network as well as a proprietary commercial image classifier from Google. Model diagnostic prediction performance in differentiating <jats:italic>normal<\/jats:italic> vs <jats:italic>nonpurulent<\/jats:italic> vs <jats:italic>purulent effusion<\/jats:italic> was scored based on classification accuracy. A web\u2010based survey was developed to test human clinicians\u2019 diagnostic accuracy on a novel image set, and this was compared head to head against our model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our model achieved a mean prediction accuracy of 80.8% (95% CI, 77.0%\u201084.6%). The Google model achieved a prediction accuracy of 85.4%. In a validation survey of 39 clinicians analyzing a sample of 22 endoscopic ear images, the average diagnostic accuracy was 65.0%. On the same data set, our model achieved an accuracy of 95.5%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our model outperformed certain groups of human clinicians in assessing images of tympanic membranes for effusions in children. Reduced diagnostic error rates using machine learning models may have implications in reducing rates of misdiagnosis, potentially leading to fewer missed diagnoses, unnecessary antibiotic prescriptions, and surgical procedures.<\/jats:p><\/jats:sec>","DOI":"10.1177\/01945998221119156","type":"journal-article","created":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T12:01:04Z","timestamp":1660651264000},"page":"41-46","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["\u201cHuman vs Machine\u201d Validation of a Deep Learning Algorithm for Pediatric Middle Ear Infection Diagnosis"],"prefix":"10.1002","volume":"169","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9950-0985","authenticated-orcid":false,"given":"Matthew G.","family":"Crowson","sequence":"first","affiliation":[{"name":"Department of Otolaryngology\u2013Head &amp; Neck Surgery Massachusetts Eye &amp; Ear  Boston Massachusetts USA"},{"name":"Department of Otolaryngology\u2013Head &amp; Neck Surgery Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David W.","family":"Bates","sequence":"additional","affiliation":[{"name":"Division of General Internal Medicine and Primary Care Brigham and Women's Hospital  Boston Massachusetts USA"},{"name":"Department of Health Policy and Management Harvard T. 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