{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:08:51Z","timestamp":1792782531158,"version":"4.5.0"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Aalborg University Hospital","id":[{"id":"https:\/\/ror.org\/02jk5qe80","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Skeletal Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate the diagnostic performance of Milvue SmartUrgences AI software for detecting hip fractures on radiographs obtained in the emergency department (ED).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective diagnostic accuracy study included consecutive patients aged\u2009\u2265\u200960 years undergoing hip and pelvis radiography at two EDs between June 2023 and September 2024. The index test was Milvue SmartUrgences, which classified radiographs as \u201cYES,\u201d \u201cNO,\u201d or \u201cDOUBT.\u201d The reference standard was established by a consensus panel of three musculoskeletal radiologists and two senior orthopaedic trauma surgeons, blinded to AI output. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1-score were calculated. The primary analysis included patients with definitive \u201cYES\u201d or \u201cNO\u201d classifications; \u201cDOUBT\u201d classifications were evaluated separately.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 539 included patients, 487 received definitive AI classifications and were included in the primary analysis. The mean age was 83.4 years (SD 8.4), and 66% were female. The reference standard identified 191 hip fractures. Sensitivity was 97.4% (95% CI 94.0\u201398.9), and specificity was 86.5% (95% CI 82.1\u201389.9). PPV was 82.3% (95% CI 76.8\u201386.7), NPV 98.1% (95% CI 95.6\u201399.2), and accuracy 90.8% (95% CI 87.9\u201393.0). The F1-score was 89.2%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Milvue SmartUrgences demonstrated high sensitivity and negative predictive value for hip fracture detection in a consecutive emergency department cohort. The software may provide useful decision support during acute radiographic assessment, but AI output should complement rather than replace clinical and radiological assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00256-026-05370-5","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T22:10:50Z","timestamp":1789423850000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evaluating artificial intelligence in the diagnosis of hip fractures: an analysis of sensitivity, specificity, positive and negative predictive values, and accuracy"],"prefix":"10.1007","author":[{"given":"Mads Hoelgaard","family":"Christensen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rasmus","family":"Elsoe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Firaz","family":"Mahdi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mate","family":"Hever","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Arif-Miscov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8094-4463","authenticated-orcid":false,"given":"Peter","family":"Larsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"5370_CR1","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1007\/s00198-006-0172-4","volume":"17","author":"O Johnell","year":"2006","unstructured":"Johnell O, Kanis JA. An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int. 2006;17:1726\u201333. https:\/\/doi.org\/10.1007\/s00198-006-0172-4.","journal-title":"Osteoporos Int"},{"key":"5370_CR2","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/BF01623184","volume":"2","author":"C Cooper","year":"1992","unstructured":"Cooper C, Campion G, Melton LJ. Hip fractures in the elderly: a world-wide projection. Osteoporos Int. 1992;2:285\u20139. https:\/\/doi.org\/10.1007\/BF01623184.","journal-title":"Osteoporos Int"},{"key":"5370_CR3","doi-asserted-by":"publisher","first-page":"e580","DOI":"10.1016\/S2666-7568(21)00172-0","volume":"2","author":"AM Wu","year":"2021","unstructured":"Wu AM, Bisignano C, James SL, Abady GG, Abedi A, Abu-Gharbieh E, et al. Global, regional, and national burden of bone fractures in 204 countries and territories, 1990\u20132019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet Healthy Longev. 2021;2:e580\u201392. https:\/\/doi.org\/10.1016\/S2666-7568(21)00172-0.","journal-title":"Lancet Healthy Longev"},{"key":"5370_CR4","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1136\/bmj.293.6556.1203","volume":"293","author":"RN Villar","year":"1986","unstructured":"Villar RN, Allen SM, Barnes SJ. Hip fractures in healthy patients: operative delay versus prognosis. Br Med J (Clin Res Ed). 1986;293:1203\u20134. https:\/\/doi.org\/10.1136\/bmj.293.6556.1203.","journal-title":"Br Med J (Clin Res Ed)"},{"key":"5370_CR5","doi-asserted-by":"publisher","first-page":"1994","DOI":"10.1001\/jama.2017.17606","volume":"318","author":"D Pincus","year":"2017","unstructured":"Pincus D, Ravi B, Wasserstein D, Huang A, Paterson JM, Nathens AB, et al. Association between wait time and 30-day mortality in adults undergoing hip fracture surgery. JAMA. 2017;318:1994\u20132003. https:\/\/doi.org\/10.1001\/jama.2017.17606.","journal-title":"JAMA"},{"key":"5370_CR6","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1148\/radiol.2020192167","volume":"296","author":"A Haj-Mirzaian","year":"2020","unstructured":"Haj-Mirzaian A, Eng J, Khorasani R, Raja AS, Levin AS, Smith SE, et al. Use of advanced imaging for radiographically occult hip fracture in elderly patients: a systematic review and meta-analysis. Radiology. 2020;296:521\u201331. https:\/\/doi.org\/10.1148\/radiol.2020192167.","journal-title":"Radiology"},{"key":"5370_CR7","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1136\/emj.18.4.263","volume":"18","author":"HR Guly","year":"2001","unstructured":"Guly HR. Diagnostic errors in an accident and emergency department. Emerg Med J. 2001;18:263\u20139. https:\/\/doi.org\/10.1136\/emj.18.4.263.","journal-title":"Emerg Med J"},{"key":"5370_CR8","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/J.AJEM.2009.07.011","volume":"29","author":"B Petinaux","year":"2011","unstructured":"Petinaux B, Bhat R, Boniface K, Aristizabal J. Accuracy of radiographic readings in the emergency department. Am J Emerg Med. 2011;29:18\u201325. https:\/\/doi.org\/10.1016\/J.AJEM.2009.07.011.","journal-title":"Am J Emerg Med"},{"key":"5370_CR9","doi-asserted-by":"publisher","first-page":"W180","DOI":"10.2214\/AJR.19.21386","volume":"213","author":"SK Selvarajan","year":"2019","unstructured":"Selvarajan SK, Levin DC, Parker L. The increasing use of emergency department imaging in the United States: is it appropriate? AJR Am J Roentgenol. 2019;213:W180-4. https:\/\/doi.org\/10.2214\/AJR.19.21386.","journal-title":"AJR Am J Roentgenol"},{"issue":"3","key":"5370_CR10","doi-asserted-by":"publisher","first-page":"e233391","DOI":"10.1001\/jamanetworkopen.2023.3391","volume":"6","author":"JR Lex","year":"2023","unstructured":"Lex JR, Di Michele J, Koucheki R, Pincus D, Whyne C, Ravi B. Artificial intelligence for hip fracture detection and outcome prediction: a systematic review and meta-analysis. JAMA Netw Open. 2023;6:e233391. https:\/\/doi.org\/10.1001\/jamanetworkopen.2023.3391.","journal-title":"JAMA Netw Open"},{"key":"5370_CR11","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1080\/17453674.2020.1803664","volume":"91","author":"Y Yamada","year":"2020","unstructured":"Yamada Y, Maki S, Kishida S, Nagai H, Arima J, Yamakawa N, et al. Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs. Acta Orthop. 2020;91:699\u2013704. https:\/\/doi.org\/10.1080\/17453674.2020.1803664.","journal-title":"Acta Orthop"},{"key":"5370_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejro.2023.100482","author":"A Parpaleix","year":"2023","unstructured":"Parpaleix A, Parsy C, Cordari M, Mejdoubi M. Assessment of a combined musculoskeletal and chest deep learning-based detection solution in an emergency setting. Eur J Radiol Open. 2023. https:\/\/doi.org\/10.1016\/j.ejro.2023.100482.","journal-title":"Eur J Radiol Open"},{"key":"5370_CR13","doi-asserted-by":"publisher","first-page":"2118","DOI":"10.1016\/j.acra.2023.06.016","volume":"30","author":"V Bousson","year":"2023","unstructured":"Bousson V, Attan\u00e9 G, Benoist N, Perronne L, Diallo A, Hadid-Beurrier L, et al. Artificial intelligence for detecting acute fractures in patients admitted to an emergency department: real-life performance of three commercial algorithms. Acad Radiol. 2023;30:2118\u201339. https:\/\/doi.org\/10.1016\/j.acra.2023.06.016.","journal-title":"Acad Radiol"},{"key":"5370_CR14","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2016","volume":"6","author":"JF Cohen","year":"2016","unstructured":"Cohen JF, Korevaar DA, Altman DG, Bruns DE, Gatsonis CA, Hooft L, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open. 2016;6:e012799. https:\/\/doi.org\/10.1136\/bmjopen-2016.","journal-title":"BMJ Open"},{"key":"5370_CR15","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1007\/s10278-021-00499-2","volume":"34","author":"J Bae","year":"2021","unstructured":"Bae J, Yu S, Oh J, Kim TH, Chung JH, Byun H, et al. External validation of deep learning algorithm for detecting and visualizing femoral neck fracture including displaced and non-displaced fracture on plain X-ray. J Digit Imaging. 2021;34:1099\u2013109. https:\/\/doi.org\/10.1007\/s10278-021-00499-2.","journal-title":"J Digit Imaging"},{"key":"5370_CR16","doi-asserted-by":"publisher","first-page":"237.e1","DOI":"10.1016\/j.crad.2019.10.022","volume":"75","author":"JS Yu","year":"2020","unstructured":"Yu JS, Yu SM, Erdal BS, Demirer M, Gupta V, Bigelow M, et al. Detection and localisation of hip fractures on anteroposterior radiographs with artificial intelligence: proof of concept. Clin Radiol. 2020;75:237.e1-237.e9. https:\/\/doi.org\/10.1016\/j.crad.2019.10.022.","journal-title":"Clin Radiol"},{"key":"5370_CR17","doi-asserted-by":"publisher","first-page":"5469","DOI":"10.1007\/s00330-019-06167-y","volume":"29","author":"C-T Cheng","year":"2019","unstructured":"Cheng C-T, Ho T-Y, Lee T-Y, Chang C-C, Chou C-C, Chen C-C, et al. Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs. Eur Radiol. 2019;29:5469\u201377. https:\/\/doi.org\/10.1007\/s00330-019-06167-y.","journal-title":"Eur Radiol"},{"key":"5370_CR18","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1111\/1754-9485.12828","volume":"63","author":"M Adams","year":"2019","unstructured":"Adams M, Chen W, Holcdorf D, McCusker MW, Howe PD, Gaillard F. Computer vs human: deep learning versus perceptual training for the detection of neck of femur fractures. J Med Imaging Radiat Oncol. 2019;63:27\u201332. https:\/\/doi.org\/10.1111\/1754-9485.12828.","journal-title":"J Med Imaging Radiat Oncol"},{"key":"5370_CR19","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020190023","volume":"2","author":"JD Krogue","year":"2020","unstructured":"Krogue JD, Cheng KV, Hwang KM, Toogood P, Meinberg EG, Geiger EJ, et al. Automatic hip fracture identification and functional subclassification with deep learning. Radiol Artif Intell. 2020;2:e190023. https:\/\/doi.org\/10.1148\/ryai.2020190023.","journal-title":"Radiol Artif Intell"},{"key":"5370_CR20","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1007\/s10278-020-00364-8","volume":"33","author":"S Mutasa","year":"2020","unstructured":"Mutasa S, Varada S, Goel A, Wong TT, Rasiej MJ. Advanced deep learning techniques applied to automated femoral neck fracture detection and classification. J Digit Imaging. 2020;33:1209\u201317. https:\/\/doi.org\/10.1007\/s10278-020-00364-8.","journal-title":"J Digit Imaging"},{"key":"5370_CR21","doi-asserted-by":"publisher","DOI":"10.1136\/tsaco-2021-000705","volume":"6","author":"J Choi","year":"2021","unstructured":"Choi J, Hui JZ, Spain D, Su Y-S, Cheng C-T, Liao C-H. Practical computer vision application to detect hip fractures on pelvic X-rays: a bi-institutional study. Trauma Surg Acute Care Open. 2021;6:e000705. https:\/\/doi.org\/10.1136\/tsaco-2021-000705.","journal-title":"Trauma Surg Acute Care Open"},{"key":"5370_CR22","doi-asserted-by":"publisher","first-page":"175","DOI":"10.5606\/ehc.2020.72163","volume":"31","author":"S Beyaz","year":"2020","unstructured":"Beyaz S, A\u00e7\u0131c\u0131 K, S\u00fcmer E. Femoral neck fracture detection in X-ray images using deep learning and genetic algorithm approaches. Jt Dis Relat Surg. 2020;31:175\u201383. https:\/\/doi.org\/10.5606\/ehc.2020.72163.","journal-title":"Jt Dis Relat Surg"},{"key":"5370_CR23","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1126\/science.3287615","volume":"240","author":"JA Swets","year":"1988","unstructured":"Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988;240:1285\u201393. https:\/\/doi.org\/10.1126\/science.3287615.","journal-title":"Science"}],"container-title":["Skeletal Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00256-026-05370-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00256-026-05370-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00256-026-05370-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:03:21Z","timestamp":1791925401000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00256-026-05370-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,14]]},"references-count":23,"alternative-id":["5370"],"URL":"https:\/\/doi.org\/10.1007\/s00256-026-05370-5","relation":{},"ISSN":["0364-2348","1432-2161"],"issn-type":[{"value":"0364-2348","type":"print"},{"value":"1432-2161","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,14]]},"assertion":[{"value":"15 June 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Obtained from the institutional review board, as no contact to patients was required for this study design.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}