{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T04:07:49Z","timestamp":1789186069425,"version":"build-2803163510"},"reference-count":29,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Laryngoscope"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To retrospectively evaluate Eyes\u2010On, a patient\u2010personalized AI\u2010assisted video analysis system for frame\u2010level detection of tracheostomy tube status and visual distress in tracheostomy\u2010dependent infants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In an IRB\u2010approved study, 25 tracheostomy\u2010dependent infants aged \u2264\u20092\u2009years underwent video recording during routine tracheostomy care and tube changes. Data were partitioned at the patient level (22 infants for model development; 3 withheld for staged evaluation). From edited clips, 10,000 frames were extracted and annotated by two blinded pediatric otolaryngologists. A YOLOv11 detector was trained to detect cannulation status, and a facial distress classifier was built using facial features and action unit signals. Generalized pretrained models were tested on held\u2010out infants and then reevaluated after patient\u2010specific calibration. Individualized thresholds were selected using decision curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The pretrained cannulation detector achieved accuracy 0.736, sensitivity 0.806, specificity 0.667, mAP@50 0.645, and a 23.62% Not\u2010Detected rate (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20091200). After calibration, pooled evaluable\u2010frame cannulation performance improved to accuracy 0.940, sensitivity 0.997, and specificity 0.874 (AUROC 0.962; AUPRC 0.918). The pretrained distress classifier achieved accuracy 0.825, sensitivity 0.877, specificity 0.770, and a 21.8% face\u2010extraction failure rate. After calibration, evaluable\u2010frame distress accuracy increased to 0.960 with sensitivity 0.974 and specificity 0.946 (AUROC 0.993; AUPRC 0.993).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In this retrospective proof\u2010of\u2010concept study, patient\u2010personalized video\u2010based AI showed promising frame\u2010level classification of tracheostomy\u2010status and facial distress. These results reflect calibrated within\u2010patient deployment and do not validate event\u2010level outcomes, alarm thresholds, or standard monitoring modalities. Prospective real\u2010world evaluation is needed before clinical adoption.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>4.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/lary.70666","type":"journal-article","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T01:56:13Z","timestamp":1780710973000},"page":"4198-4208","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>AI<\/scp>\n                    \u2010Assisted Video Monitoring for Tracheostomy\u2010Dependent Infants: A Proof\u2010Of\u2010Concept Study"],"prefix":"10.1002","volume":"136","author":[{"given":"Colleen F.","family":"Cecola","sequence":"first","affiliation":[{"name":"Louisiana State University Health Sciences Center  New Orleans Louisiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine","family":"Settoon","sequence":"additional","affiliation":[{"name":"Louisiana State University Health Sciences Center  New Orleans Louisiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren S.","family":"Buck","sequence":"additional","affiliation":[{"name":"Louisiana State University Health Sciences Center  New Orleans Louisiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adele K.","family":"Evans","sequence":"additional","affiliation":[{"name":"University of Central Florida College of Medicine  Orlando Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4972-8137","authenticated-orcid":false,"given":"Michael E.","family":"Dunham","sequence":"additional","affiliation":[{"name":"Louisiana State University Health Sciences Center  New Orleans Louisiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,5]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3389\/fped.2021.661512"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen\u20102019\u2010031816"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamapediatrics.2022.1069"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1542\/peds.2023\u2010064920"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.202508\u20102055ST"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijporl.2020.110563"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/lary.30674"},{"key":"e_1_2_10_9_1","doi-asserted-by":"crossref","unstructured":"J.Redmon S.Divvala R.Girshick andA.Farhadi \u201cYou Only Look Once: Unified Real\u2010Time Object Detection \u201dinProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2016) 779\u2013788 https:\/\/doi.org\/10.1109\/CVPR.2016.91.","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_2_10_10_1","doi-asserted-by":"crossref","unstructured":"L.Lu D.He C.Liu andZ.Deng \u201cMASF\u2010YOLO: An Improved YOLOv11 Network for Small Object Detection on Drone View \u201darXiv:2504.18136 2025 https:\/\/doi.org\/10.48550\/arXiv.2504.18136.","DOI":"10.1109\/SMC58881.2025.11342487"},{"key":"e_1_2_10_11_1","doi-asserted-by":"crossref","unstructured":"J.Deng J.Guo E.Ververas I.Kotsia andS.Zafeiriou \u201cRetinaFace: Single\u2010Shot Multi\u2010Level Face Localisation in the Wild \u201dinProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(2020) 5203\u20135212 https:\/\/doi.org\/10.1109\/CVPR42600.2020.00525.","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"e_1_2_10_12_1","volume-title":"Facial Action Coding System: A Technique for the Measurement of Facial Movement","author":"Ekman P.","year":"1978"},{"key":"e_1_2_10_13_1","unstructured":"O.SenerandS.Savarese \u201cActive Learning for Convolutional Neural Networks: A Core\u2010Set Approach \u201darXiv:1708.00489 2017 https:\/\/doi.org\/10.48550\/arXiv.1708.00489."},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304\u20103975(85)90224\u20105"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X06295361"},{"key":"e_1_2_10_16_1","unstructured":"A.Stock \u201cStock Photos Stock Photography and Royalty\u2010Free Images \u201daccessed May 13 2026 https:\/\/stock.adobe.com\/photos."},{"key":"e_1_2_10_17_1","unstructured":"M. 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