{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T12:57:14Z","timestamp":1790686634616,"version":"4.1.0"},"reference-count":41,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aao-hnsfjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Otolaryngol.--head neck surg."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop an artificial intelligence (AI) system for craniofacial morphology analysis in pediatric obstructive sleep apnea (OSA) using photogrammetry.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Prospective, cross\u2010sectional study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Tertiary medical hospital.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Children aged 3 to 18 years with OSA\u2010related symptoms were enrolled and underwent overnight polysomnography (PSG) and standardized craniofacial photogrammetry. Moderate\u2010to\u2010severe OSA in children was defined as an apnea\u2010hypopnea index (AHI)\u2009\u2265\u20095 events\/h in PSG. An AI model using the Dlib tool identified facial landmarks, and the Hough transform calculated variables from these coordinates. Measurements by humans, the AI model, and a manually adjusted AI model were compared. Random forest identified the top 10 variable importance for the OSA prediction model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Forty\u2010three children with moderate\u2010to\u2010severe OSA and 43 age\u2010, gender\u2010, and obesity\u2010matched controls were included. Shared predictors across models were mandibular plane angle, maxillary\u2010mandibular relationship, lower facial length, and lower facial proportion. Additional predictors for the AI model included lower\u2010 and mid\u2010face projection, while the adjusted AI model added mid\u2010face projection, retrusive mandible, and cervicomental angle. The area under the curve (AUC) values for moderate\u2010to\u2010severe OSA prediction were similar in human, AI model, and adjusted AI model (0.74 vs 0.71 vs 0.70,\n                      <jats:italic>P<\/jats:italic>\n                      for \u0394AUC\u2009&gt;\u20090.05). The AI model significantly reduced measurement time (human vs AI vs adjusted AI\u2009=\u2009511.4 vs 0.85 vs 15.8\u2009seconds,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u2009.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The AI\u2010powered photogrammetry analysis system is a rapid and reliable tool with comparable performance to human measurements in evaluating pediatric OSA.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ohn.70463","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T12:13:31Z","timestamp":1790684011000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence\u2010Powered Craniofacial Photogrammetry Analysis of Pediatric Obstructive Sleep Apnea"],"prefix":"10.1002","author":[{"given":"Wan\u2010Yi","family":"Hsueh","sequence":"first","affiliation":[{"name":"Department of Otolaryngology Hsinchu Cathay General Hospital Hsinchu Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4421-9261","authenticated-orcid":false,"given":"Kun\u2010Tai","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology and Sleep Center Taipei Hospital, Ministry of Health and Welfare New Taipei City Taiwan"},{"name":"Department of Otolaryngology National Taiwan University Hospital and Children's Hospital Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8583-8459","authenticated-orcid":false,"given":"Shih\u2010Hsu","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering Chung Yuan Christian University Taoyuan Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chia\u2010Jo","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering Chung Yuan Christian University Taoyuan Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chih\u2010Wen","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Information &amp; 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