{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T01:09:07Z","timestamp":1790212147198,"version":"4.1.0"},"reference-count":0,"publisher":"SPIE-Intl Soc Optical Eng","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Med. Imag."],"published-print":{"date-parts":[[2023,4,25]]},"abstract":"<jats:p>PurposeTo integrate and evaluate an artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest x-rays (CXRs) in clinical practice.ApproachIn clinical use over 17 months, 214 CXR images were ordered to check ETT placement with AI assistance by intensive care unit (ICU) physicians. The system was built on the SimpleMind Cognitive AI platform and integrated into a clinical workflow. It automatically identified the ETT and checked its placement relative to the trachea and carina. The ETT overlay and misplacement alert messages generated by the AI system were compared with radiology reports as the reference. A survey study was also conducted to evaluate usefulness of the AI system in clinical practice.ResultsThe alert messages indicating that either the ETT was misplaced or not detected had a positive predictive value of 42% (21\/50) and negative predictive value of 98% (161\/164) based on the radiology reports. In the survey, radiologist and ICU physician users indicated that they agreed with the AI outputs and that they were useful.ConclusionsThe AI system performance in real-world clinical use was comparable to that seen in previous experiments. Based on this and physician survey results, the system can be deployed more widely at our institution, using insights gained from this evaluation to make further algorithm improvements and quality assurance of the AI system.<\/jats:p>","DOI":"10.1117\/1.jmi.10.5.051805","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T13:23:00Z","timestamp":1682428980000},"page":"1-11","source":"Crossref","is-referenced-by-count":6,"title":["Integration and evaluation of chest X-ray artificial intelligence in clinical practice"],"prefix":"10.1117","volume":"10","author":[{"given":"Koon-Pong","family":"Wong","sequence":"first","affiliation":[{"name":"UCLA Ctr. for Computer Vision & Imaging Biomarkers"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suzanne Y.","family":"Homer","sequence":"additional","affiliation":[{"name":"UCLA Ctr. for Computer Vision & Imaging Biomarkers"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sindy H.","family":"Wei","sequence":"additional","affiliation":[{"name":"UCLA Ctr. for Computer Vision & Imaging Biomarkers"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nazanin","family":"Yaghmai","sequence":"additional","affiliation":[{"name":"UCLA Ctr. for Computer Vision & Imaging Biomarkers"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8689-9317","authenticated-orcid":false,"given":"Oscar A.","family":"Estrada Paz","sequence":"additional","affiliation":[{"name":"UCLA Ctr. for Computer Vision & Imaging Biomarkers"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy J.","family":"Young","sequence":"additional","affiliation":[{"name":"UCLA Ctr. for Computer Vision & Imaging Biomarkers"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Russell G.","family":"Buhr","sequence":"additional","affiliation":[{"name":"UCLA Ctr. for Computer Vision & Imaging Biomarkers"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Igor","family":"Barjaktarevic","sequence":"additional","affiliation":[{"name":"UCLA Ctr. for Computer Vision & Imaging Biomarkers"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liza","family":"Shrestha","sequence":"additional","affiliation":[{"name":"UCLA Ctr. for Computer Vision & Imaging Biomarkers"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8128-6746","authenticated-orcid":false,"given":"Morgan","family":"Daly","sequence":"additional","affiliation":[{"name":"UCLA Ctr. for Computer Vision & Imaging Biomarkers"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Goldin","sequence":"additional","affiliation":[{"name":"UCLA Ctr. for Computer Vision & Imaging Biomarkers"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dieter R.","family":"Enzmann","sequence":"additional","affiliation":[{"name":"UCLA Ctr. for Computer Vision & Imaging Biomarkers"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew S.","family":"Brown","sequence":"additional","affiliation":[{"name":"UCLA Ctr. for Computer Vision & Imaging Biomarkers"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"189","container-title":["Journal of Medical Imaging"],"original-title":[],"link":[{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-10\/issue-05\/051805\/Integration-and-evaluation-of-chest-X-ray-artificial-intelligence-in\/10.1117\/1.JMI.10.5.051805.full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:45:16Z","timestamp":1790210716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-10\/issue-05\/051805\/Integration-and-evaluation-of-chest-X-ray-artificial-intelligence-in\/10.1117\/1.JMI.10.5.051805.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,25]]},"references-count":0,"journal-issue":{"issue":"05","published-online":{"date-parts":[[2023,9,1]]}},"URL":"https:\/\/doi.org\/10.1117\/1.jmi.10.5.051805","relation":{},"ISSN":["2329-4302","2329-4310"],"issn-type":[{"value":"2329-4302","type":"print"},{"value":"2329-4310","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,25]]}}}