{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T16:27:42Z","timestamp":1785342462305,"version":"3.55.0"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"1134","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,1]]},"abstract":"<jats:sec>\n                  <jats:title>Objectives:<\/jats:title>\n                  <jats:p>Trauma chest radiographs may contain subtle and time-critical pathology. Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist prioritisation. However, few AI programs have been externally validated. This study aimed to evaluate the performance of a commercially available deep convolutional neural network \u2013 Annalise CXR V1.2 (Annalise.ai) \u2013 for detection of traumatic injuries on supine chest radiographs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods:<\/jats:title>\n                  <jats:p>Chest radiographs with a CT performed within 24\u2009h in the setting of trauma were retrospectively identified at a level one adult trauma centre between January 2009 and June 2019. Annalise.ai assessment of the chest radiograph was compared to the radiologist report of the chest radiograph. Contemporaneous CT report was taken as the ground truth. Agreement with CT was measured using Cohen\u2019s \u03ba and sensitivity\/specificity for both AI and radiologists were calculated.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results:<\/jats:title>\n                  <jats:p>There were 1404 cases identified with a median age of 52 (IQR 33\u201369) years, 949 males. AI demonstrated superior performance compared to radiologists in identifying pneumothorax (p = 0.007) and segmental collapse (p = 0.012) on chest radiograph. Radiologists performed better than AI for clavicle fracture (p = 0.002), humerus fracture (p &amp;lt; 0.0015) and scapula fracture (p = 0.014). No statistical difference was found for identification of rib fractures and pneumomediastinum.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion:<\/jats:title>\n                  <jats:p>The evaluated AI performed comparably to radiologists in interpreting chest radiographs. Further evaluation of this AI program has the potential to enable it to be safely incorporated in clinical processes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Advances in knowledge:<\/jats:title>\n                  <jats:p>Clinically useful AI programs represent promising decision support tools.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1259\/bjr.20210979","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T17:27:34Z","timestamp":1646933254000},"source":"Crossref","is-referenced-by-count":32,"title":["Diagnostic accuracy of a commercially available deep-learning algorithm in supine chest radiographs following trauma"],"prefix":"10.1093","volume":"95","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0761-5975","authenticated-orcid":false,"given":"Jacob","family":"Gipson","sequence":"first","affiliation":[{"name":"Department of Radiology, Alfred Health , Melbourne, Victoria , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Alfred Health , Melbourne, Victoria , Australia"},{"name":"Faculty of Medicine, University of Queensland , Brisbane, Queensland , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jarrel","family":"Seah","sequence":"additional","affiliation":[{"name":"Department of Radiology, Alfred Health , Melbourne, Victoria , Australia"},{"name":"Harrison.ai , Sydney, NSW , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helen","family":"Kavnoudias","sequence":"additional","affiliation":[{"name":"Department of Radiology, Alfred Health , Melbourne, Victoria , Australia"},{"name":"Department of Surgery, Monash University , Melbourne, Victoria , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adil","family":"Zia","sequence":"additional","affiliation":[{"name":"Department of Radiology, Alfred Health , Melbourne, Victoria , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robin","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiology, Alfred Health , Melbourne, Victoria , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biswadev","family":"Mitra","sequence":"additional","affiliation":[{"name":"National Trauma Research Institute , Melbourne, Victoria , Australia"},{"name":"Emergency & Trauma Centre, The Alfred Hospital , Melbourne, Victoria , Australia"},{"name":"School of Public Health & Preventive Medicine, Monash University , Melbourne, Victoria , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Warren","family":"Clements","sequence":"additional","affiliation":[{"name":"Department of 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