Abstract

* Abbreviations: AI — : artificial intelligence AOM — : acute otitis media OCT — : optical coherence tomography OME — : otitis media with effusion TM — : tympanic membrane In this issue of Pediatrics , Crowson et al1 provide a glimpse to the future for otitis media (OM) diagnosis, perhaps. Data acquisition from 126 normal and 212 abnormal ear images met study quality criteria when children were brought to the operating room for an examination under anesthesia for tympanostomy tube placement for either recurrent acute otitis media (AOM) or otitis media with effusion (OME). To meet the quality criteria, 75% of the surface area of the tympanic membrane (TM) had to be visible and there had to be sufficient resolution to assess major landmarks. The otolaryngologist classified each ear at the time of myringotomy as effusion present or absent. If an effusion was present, the effusion was categorized as either serous, serous/mucoid, mucopurulent, or purulent. However, the deep learning model was used to target only a binary output of no effusion as normal or any effusion or any degree of TM pathology as abnormal. The model achieved a mean image classification accuracy of 83.8% (95% confidence interval: 82.7%–84.8%). In the hands of an experienced and trained clinician intent on making a correct diagnosis, … Address correspondence to Michael E. Pichichero, MD, Research Institute at Rochester General Hospital, 1425 Portland Ave, Rochester, NY 14621. E-mail: michael.pichichero{at}rochesterregional.org

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