{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T14:55:09Z","timestamp":1783090509875,"version":"3.54.6"},"reference-count":21,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation program","award":["No 954221 for the EIC SME Instrument project AutoR"],"award-info":[{"award-number":["No 954221 for the EIC SME Instrument project AutoR"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Acta Radiol"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Assessment of subtle hip fractures on radiographs can be difficult, especially among less experienced emergency physicians, which may prolong the diagnosis and ultimately time to surgery. Clinical artificial intelligence (AI) decision support tools have shown great potential in assisting the detection of fractures on radiographs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To investigate how a CE-marked AI fracture detection tool affects junior doctors\u2019 diagnostic accuracy in detecting hip fractures on radiographs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and Methods<\/jats:title>\n                    <jats:p>Eight junior doctors with affiliation to the Accident and Emergency (A&amp;E) department read 246 hip radiographic examinations with and without AI support. The reference standard was determined by two musculoskeletal radiologists, to measure sensitivity and specificity for readers without and with support from the AI tool as well as the AI tool's standalone performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Mean sensitivity in detecting hip fractures increased significantly from 0.89 (95% confidence interval [CI] = 0.85\u20130.93) without AI support to 0.94 (95% CI = 0.92\u20130.97) (\u03c7\n                      <jats:sup>2<\/jats:sup>\n                      = 9.27;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009=\u20090.002) with AI support and the false-negative cases was thereby reduced by 49%. There was no significant change in mean specificity 0.90 (95% CI = 0.86\u20130.93) to 0.91 (95% CI = 0.88\u20130.94) (\u03c7\n                      <jats:sup>2<\/jats:sup>\n                      = 0.34;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009=\u20090.56). The AI standalone performance was 0.99 (95% CI = 0.99\u20131.00) and 0.73 (95% CI = 0.67\u20130.80) in sensitivity and specificity, respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Out of eight junior doctors, seven detected more fractures with AI assistance than without. The applied performance gain for readers highlights the value of the product.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/02841851261417239","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T18:06:49Z","timestamp":1770660409000},"page":"314-323","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Hip fracture detection on radiographs using an artificial intelligence\u2013based support tool: a diagnostic accuracy study"],"prefix":"10.1177","volume":"67","author":[{"given":"Nicholas","family":"Bonde","sequence":"first","affiliation":[{"name":"Department of Orthopaedic Surgery and Traumatology, Copenhagen University Hospital, Bispebjerg, K\u00f8benhavn NV, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristian","family":"Kj\u00e6rgaard","sequence":"additional","affiliation":[{"name":"Odense University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henriette","family":"Aunaas","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery and Traumatology, Copenhagen University Hospital, Bispebjerg, K\u00f8benhavn NV, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stine","family":"Hangaard","sequence":"additional","affiliation":[{"name":"Department of Radiology, Copenhagen University Hospital, Herlev\/Gentofte, Herlev, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1543-4293","authenticated-orcid":false,"given":"Cecilie","family":"Daugaard","sequence":"additional","affiliation":[{"name":"Department of Radiology, Copenhagen University Hospital, Amager\/Hvidovre, Hvidovre, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janus","family":"Nybing","sequence":"additional","affiliation":[{"name":"Department of Radiology and Radiological AI Testcenter (RAIT) Denmark, Copenhagen University Hospital, K\u00f8benhavn NV, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikael","family":"Boesen","sequence":"additional","affiliation":[{"name":"Department of Radiology and Radiological AI Testcenter (RAIT) Denmark, Copenhagen University Hospital, K\u00f8benhavn NV, Denmark"},{"name":"Department of Clinical Medicine, Faculty of Health, and Medical Sciences, University of Copenhagen, K\u00f8benhavn K, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4371-8306","authenticated-orcid":false,"given":"Rikke","family":"Bachmann","sequence":"additional","affiliation":[{"name":"Department of Radiology and Radiological AI Testcenter (RAIT) Denmark, Copenhagen University Hospital, K\u00f8benhavn NV, Denmark"},{"name":"Radiobotics, ApS, Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4216-5757","authenticated-orcid":false,"given":"Michael","family":"Lundemann","sequence":"additional","affiliation":[{"name":"Radiobotics, ApS, Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00f8ren","family":"Overgaard","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery and Traumatology, Copenhagen University Hospital, Bispebjerg, K\u00f8benhavn NV, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"370169","article-title":"Radiographically occult and subtle fractures: a pictorial review","volume":"2013","author":"Jarraya M","year":"2013","unstructured":"Jarraya M, Hayashi D, Roemer FW, et al. Radiographically occult and subtle fractures: a pictorial review. Radiol Res Pract 2013;2013:370169.","journal-title":"Radiol Res Pract"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020192167"},{"key":"e_1_3_3_4_2","first-page":"111","article-title":"Traumatic fractures in adults: missed diagnosis on plain radiographs in the emergency department","volume":"89","author":"Pinto A","year":"2018","unstructured":"Pinto A, Berritto D, Russo A, et al. Traumatic fractures in adults: missed diagnosis on plain radiographs in the emergency department. Acta Biomed 2018;89:111\u2013123.","journal-title":"Acta Biomed"},{"key":"e_1_3_3_5_2","unstructured":"National Institute for Health and Care Excellence. Hip fracture: management. London: NICE 2011. Available at: https:\/\/www.nice.org.uk\/guidance\/cg124\/resources\/hip-fracture-management-pdf-35109449902789 (accessed 6 January 2023)."},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1302\/0301-620X.103B7.BJJ-2020-2582.R1"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.211785"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/17453674.2017.1344459"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00256-018-3016-3"},{"key":"e_1_3_3_10_2","doi-asserted-by":"crossref","unstructured":"Bachmann R Gunes G Hangaard S et al. Improving traumatic fracture detection on radiographs with artificial intelligence support: a multi-reader study. BJR Open 2024;6:tzae011.","DOI":"10.1093\/bjro\/tzae011"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.210937"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2021203886"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12891-021-04260-2"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.210064"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2016-012799"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11999.0000000000000066"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1177\/21514593211056739"},{"key":"e_1_3_3_18_2","article-title":"Requirements for minimum sample size for sensitivity and specificity analysis","volume":"10","author":"Bujang MA","year":"2016","unstructured":"Bujang MA, Adnan TH. Requirements for minimum sample size for sensitivity and specificity analysis. J Clin Diagn Res 2016;10:YE01\u2013YE06.","journal-title":"J Clin Diagn Res"},{"key":"e_1_3_3_19_2","article-title":"Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: A diagnostic accuracy study","volume":"4","author":"Oakden-Rayner L","year":"2022","unstructured":"Oakden-Rayner L, Gale W, Bonham TA, et al. Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: A diagnostic accuracy study. Lancet Digit Health 2022;4:e351\u2013e358.","journal-title":"Lancet Digit Health"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-006-0265-9"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1136\/emermed-2019-208446"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.injury.2020.09.063"}],"container-title":["Acta Radiologica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/02841851261417239","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/02841851261417239","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/02841851261417239","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:34:59Z","timestamp":1777390499000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/02841851261417239"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,9]]},"references-count":21,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["10.1177\/02841851261417239"],"URL":"https:\/\/doi.org\/10.1177\/02841851261417239","relation":{},"ISSN":["0284-1851","1600-0455"],"issn-type":[{"value":"0284-1851","type":"print"},{"value":"1600-0455","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,9]]}}}