{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T14:52:09Z","timestamp":1785768729287,"version":"3.56.0"},"reference-count":43,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aao-hnsfjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Otolaryngol.--head neck surg."],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Otitis media is the leading cause of healthcare visits and antibiotic prescriptions for children in the United States. Differentiating acute otitis media (AOM) from otitis media with effusion (OME) is crucial for antibiotic stewardship but is often difficult. The objective was to train an artificial intelligence algorithm that accurately predicts the presence and nature of middle ear effusion in pediatric patients using pediatric tympanic membrane (TM) images captured with inexpensive, consumer\u2010grade otoscopes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Prospective cohort study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Tertiary Children's Hospitals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A multicenter study gathered ear images from children aged 6 months to 10 years undergoing myringotomy and tube placement at four pediatric hospitals in the United States. Images were taken with over\u2010the\u2010counter digital otoscopes. Intraoperative middle ear findings were used to label the images. A deep learning algorithm was trained to classify middle ear disease. Performance was assessed by weighted accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>From a diverse population of 219 children (42.14% black, Hispanic, Asian, and other), 737 images were obtained, categorized as AOM (73), OME (190), no effusion or infection (274), and no TM in image (200). The classification model achieved a weighted accuracy of 92.5%, ranging 88.4% to 98.8% per individual category.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The model demonstrated high accuracy in classifying the middle ear state in young, anesthetized children. Developing an effective deep learning model using diverse, age\u2010representative images from affordable digital otoscopes may move us closer to real\u2010world applications of such technology in clinical practice to validate the role of telemedicine and improve antibiotic stewardship.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ohn.70031","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T06:05:23Z","timestamp":1758002723000},"page":"1485-1493","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Toward an Unbiased Deep Learning Classifier of Pediatric Middle Ear Disease"],"prefix":"10.1002","volume":"173","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8946-6520","authenticated-orcid":false,"given":"Sruthi","family":"Surapaneni","sequence":"first","affiliation":[{"name":"College of Human Medicine Michigan State University Rochester Hills Michigan 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