<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Miller ME</submitter><funding>NIBIB NIH HHS</funding><funding>NIA NIH HHS</funding><funding>ODCDC CDC HHS</funding><funding>Common Fund</funding><funding>National Institute for Biomedical Imaging and Bioengineering</funding><funding>NIH HHS</funding><pagination>1046-1053</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11832335</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>135(3)</volume><pubmed_abstract>&lt;h4>Objectives&lt;/h4>Here we describe the development and pilot testing of the first artificial intelligence (AI) software "copilot" to help train novices to competently perform flexible fiberoptic laryngoscopy (FFL) on a mannikin and improve their uptake of FFL skills.&lt;h4>Methods&lt;/h4>Supervised machine learning was used to develop an image classifier model, dubbed the "anatomical region classifier," responsible for predicting the location of camera in the upper aerodigestive tract and an object detection model, dubbed the "anatomical structure detector," 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 connec</pubmed_abstract><journal>The Laryngoscope</journal><pubmed_title>Development of Machine Learning Copilot to Assist Novices in Learning Flexible Laryngoscopy.</pubmed_title><pmcid>PMC11832335</pmcid><funding_grant_id>OT2 OD032720</funding_grant_id><funding_grant_id>K76 AG079040</funding_grant_id><funding_grant_id>1R41EB034186-01</funding_grant_id><funding_grant_id>R41 EB034186</funding_grant_id><pubmed_authors>Lina I</pubmed_authors><pubmed_authors>Witte D</pubmed_authors><pubmed_authors>Rameau A</pubmed_authors><pubmed_authors>Walsh J</pubmed_authors><pubmed_authors>Bhatti NI</pubmed_authors><pubmed_authors>Miller ME</pubmed_authors></additional><is_claimable>false</is_claimable><name>Development of Machine Learning Copilot to Assist Novices in Learning Flexible Laryngoscopy.</name><description>&lt;h4>Objectives&lt;/h4>Here we describe the development and pilot testing of the first artificial intelligence (AI) software "copilot" to help train novices to competently perform flexible fiberoptic laryngoscopy (FFL) on a mannikin and improve their uptake of FFL skills.&lt;h4>Methods&lt;/h4>Supervised machine learning was used to develop an image classifier model, dubbed the "anatomical region classifier," responsible for predicting the location of camera in the upper aerodigestive tract and an object detection model, dubbed the "anatomical structure detector," 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 connec</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Mar</publication><modification>2026-07-12T03:14:43.519Z</modification><creation>2026-07-12T03:11:12.268Z</creation></dates><accession>S-EPMC11832335</accession><cross_references><pubmed>39363661</pubmed><doi>10.1002/lary.31812</doi></cross_references></HashMap>