{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T16:06:58Z","timestamp":1779206818155,"version":"3.51.4"},"reference-count":34,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100015326","name":"Common Fund","doi-asserted-by":"publisher","award":["OT2 OD032720"],"award-info":[{"award-number":["OT2 OD032720"]}],"id":[{"id":"10.13039\/100015326","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Laryngoscope"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>Here we describe the development and pilot testing of the first artificial intelligence (AI) software \u201ccopilot\u201d to help train novices to competently perform flexible fiberoptic laryngoscopy (FFL) on a mannikin and improve their uptake of FFL skills.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Supervised machine learning was used to develop an image classifier model, dubbed the \u201canatomical region classifier,\u201d responsible for predicting the location of camera in the upper aerodigestive tract and an object detection model, dubbed the \u201canatomical structure detector,\u201d responsible for locating and identifying key anatomical structures in images. Training data were collected by performing FFL on an AirSim Combo Bronchi X mannikin (United Kingdom, TruCorp Ltd) using an Ambu aScope 4 RhinoLaryngo Slim connected to an Ambu\u00ae aView\u2122 2 Advance Displaying Unit (Ballerup, Ambu A\/S). Medical students were prospectively recruited to try the FFL copilot and rate its ease of use and self\u2010rate their skills with and without the copilot.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>This model classified anatomical regions with an overall accuracy of 91.9% on the validation set and 80.1% on the test set. The model detected anatomical structures with overall mean average precision of 0.642. Through various optimizations, we were able to run the AI copilot at approximately 28 frames per second (FPS), which is imperceptible from real time and nearly matches the video frame rate of 30 FPS. Sixty\u2010four novice medical students were recruited for feedback on the copilot. Although 90.9% strongly agreed\/agreed that the AI copilot was easy to use, their self\u2010rating of FFL skills following use of the copilot were overall equivocal to their self\u2010rating without the copilot.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The AI copilot tracked successful capture of diagnosable views of key anatomical structures effectively guiding users through FFL to ensure all anatomical structures are sufficiently captured. This tool has the potential to assist novices in efficiently gaining competence in FFL.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>NA <jats:italic>Laryngoscope<\/jats:italic>, 135:1046\u20131053, 2025<\/jats:p><\/jats:sec>","DOI":"10.1002\/lary.31812","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T06:44:02Z","timestamp":1728024242000},"page":"1046-1053","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Development of Machine Learning Copilot to Assist Novices in Learning Flexible Laryngoscopy"],"prefix":"10.1002","volume":"135","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5649-9996","authenticated-orcid":false,"given":"Mattea E.","family":"Miller","sequence":"first","affiliation":[{"name":"Department of Otolaryngology\u2010Head and Neck Surgery Johns Hopkins School of Medicine  Baltimore Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Witte","sequence":"additional","affiliation":[{"name":"Perceptron Health, Inc  New York New York U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8619-7312","authenticated-orcid":false,"given":"Ioan","family":"Lina","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2010Head and Neck Surgery Johns Hopkins School of Medicine  Baltimore Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9604-6721","authenticated-orcid":false,"given":"Jonathan","family":"Walsh","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2010Head and Neck Surgery Johns Hopkins School of Medicine  Baltimore Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1543-2634","authenticated-orcid":false,"given":"Ana\u00efs","family":"Rameau","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology \u2013 Head and Neck Surgery, Sean Parker Institute for the Voice Weill Cornell Medical College  New York New York U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3355-0549","authenticated-orcid":false,"given":"Nasir I.","family":"Bhatti","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2010Head and Neck Surgery Johns Hopkins School of Medicine  Baltimore Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"e_1_2_6_2_1","volume-title":"The Complexities of Physician Supply and Demand: Projections from 2021 to 2036","author":"GlobalData Plc","year":"2024"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0b013e3181b6c90b"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1067\/mhn.2000.109761"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1177\/0194599820908860"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.otc.2020.05.016"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1177\/014556131609500607"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/lary.26188"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1177\/0194599820913495"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvoice.2021.05.002"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1748-0361.2010.00343.x"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/lary.28652"},{"key":"e_1_2_6_13_1","volume-title":"StatPearls","author":"Alvi S","year":"2024"},{"key":"e_1_2_6_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2020.05.009"},{"key":"e_1_2_6_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/lary.21063"},{"issue":"4","key":"e_1_2_6_16_1","article-title":"Simulation\u2010based surgical training needs in otorhinolaryngology","volume":"69","author":"L\u00fcscher M","year":"2022","journal-title":"Dan Med J"},{"key":"e_1_2_6_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00405-024-08512-4"},{"key":"e_1_2_6_18_1","unstructured":"RamirezS.FastAPI.https:\/\/fastapi.tiangolo.com."},{"key":"e_1_2_6_19_1","unstructured":"HeK ZhangX RenS SunJ.Deep residual learning for image recognition. 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