{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T21:09:08Z","timestamp":1789247348322,"version":"build-2803163510"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Trauma Emerg Surg"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Distal radius fractures (DRFs) are often initially assessed by junior doctors under time constraints, with limited supervision, risking significant consequences if missed. Convolutional Neural Networks (CNNs) can aid in diagnosing fractures. This study aims to internally and externally validate an open source algorithm for the detection and localization of DRFs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Patients from a level 1 trauma center from Adelaide, Australia\u00a0that presented between 2016 and 2020 with wrist trauma were retrospectively included. Radiographs were reviewed confirming the presence or absence of a fracture, as well as annotating radius, ulna, and fracture location. An internal validation dataset from the same hospital was created. An external validation set was created with two other level 1 trauma centers, from Groningen and Rotterdam, the Netherlands. Three surgeons reviewed both sets for DRFs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The algorithm was trained on 659 radiographs. The internal validation set included 190 patients, showing an accuracy of 87% and an AUC of 0.93 for DRF detection. The external validation set consisted of 188 patients, with an accuracy and AUC were 82% and 0.88 respectively. Radial and ulnar bone segmentation on the internal validation was excellent with an AP50 of 99 and 98, but moderate for fracture segmentation with an AP50 of 29. For external validation the AP50 was 92, 89 and 25 for radius, ulna, and fracture respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This open-source algorithm effectively detects DRFs with high accuracy and localizes them with moderate accuracy. It can assist clinicians in diagnosing suspected DRFs and is the first radiograph-based CNN externally validated on patients from multiple hospitals.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00068-024-02731-4","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T09:20:57Z","timestamp":1737105657000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An open source convolutional neural network to detect and localize distal radius fractures on plain radiographs"],"prefix":"10.1007","volume":"51","author":[{"given":"Koen D.","family":"Oude Nijhuis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Britt","family":"Barvelink","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasper","family":"Prijs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibin","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruurd L.","family":"Jaarsma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank F. A.","family":"IJpma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joost W.","family":"Colaris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Job N.","family":"Doornberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathieu M. E.","family":"Wijffels","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"On behalf of the Machine Learning Consortium","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hans","family":"van Luit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Canta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanne","family":"Hoeksema","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charlotte L.E.","family":"Laane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaan","family":"Aksakal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haras","family":"Mhmud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Jutte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Gordon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wouter","family":"Mallee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Duckworth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niels","family":"Schep","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Hendrickx","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne-Eva","family":"Bulstra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lente","family":"Dankelman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Reijman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"2731_CR1","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1053\/jhsu.2001.26322","volume":"26","author":"KC Chung","year":"2001","unstructured":"Chung KC, Spilson SV. The frequency and epidemiology of hand and forearm fractures in the United States. Journal of Hand Surgery. 2001;26:908\u201315.","journal-title":"Journal of Hand Surgery"},{"key":"2731_CR2","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1080\/01421590903143211","volume":"31","author":"Z Ahmad","year":"2009","unstructured":"Ahmad Z. Letters to the editor. Med Teach. 2009;31:672\u20134.","journal-title":"Med Teach"},{"key":"2731_CR3","doi-asserted-by":"crossref","unstructured":"Guly HR (2001) Diagnostic errors in an accident and emergency department [Internet]. Emerg Med J Available from: www.emjonline.com","DOI":"10.1136\/emj.18.4.263"},{"key":"2731_CR4","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1148\/radiol.12111119","volume":"266","author":"JS Whang","year":"2013","unstructured":"Whang JS, Baker SR, Patel R, Luk L, Castro A. The causes of medical malpractice suits against radiologists in the United States. Radiology. 2013;266:548\u201354.","journal-title":"Radiology"},{"issue":"10","key":"2731_CR5","doi-asserted-by":"publisher","first-page":"2420","DOI":"10.3390\/diagnostics12102420","volume":"12","author":"T Meena","year":"2022","unstructured":"Meena T, Roy S. bone fracture detection using deep supervised learning from radiological images: a paradigm shift. Diagnostics (Basel). 2022;12(10):2420.","journal-title":"Diagnostics (Basel)"},{"key":"2731_CR6","doi-asserted-by":"publisher","first-page":"108925","DOI":"10.1016\/j.ejrad.2020.108925","volume":"126","author":"C Bl\u00fcthgen","year":"2020","unstructured":"Bl\u00fcthgen C, Becker AS, Vittoria de Martini I, Meier A, Martini K, Frauenfelder T. Detection and localization of distal radius fractures: deep learning system versus radiologists. Eur J Radiol. 2020;126:108925.","journal-title":"Eur J Radiol"},{"key":"2731_CR7","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1080\/17453674.2019.1600125","volume":"90","author":"K Gan","year":"2019","unstructured":"Gan K, Xu D, Lin Y, Shen Y, Zhang T, Hu K, et al. Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Acta Orthop. 2019;90:394\u2013400.","journal-title":"Acta Orthop"},{"key":"2731_CR8","doi-asserted-by":"crossref","unstructured":"Kim DH, MacKinnon T (2018) Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol [Internet]. 2018;73:439\u201345. Available from: https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0009926017305354","DOI":"10.1016\/j.crad.2017.11.015"},{"key":"2731_CR9","doi-asserted-by":"publisher","first-page":"11591","DOI":"10.1073\/pnas.1806905115","volume":"115","author":"R Lindsey","year":"2018","unstructured":"Lindsey R, Daluiski A, Chopra S, Lachapelle A, Mozer M, Sicular S, et al. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A. 2018;115:11591\u20136.","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2731_CR10","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1080\/17453674.2017.1344459","volume":"88","author":"J Olczak","year":"2017","unstructured":"Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, et al. Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms\u2014are they on par with humans for diagnosing fractures? Acta Orthop. 2017;88:581\u20136.","journal-title":"Acta Orthop"},{"key":"2731_CR11","doi-asserted-by":"publisher","first-page":"e180001","DOI":"10.1148\/ryai.2019180001","volume":"1","author":"YL Thian","year":"2019","unstructured":"Thian YL, Li Y, Jagmohan P, Sia D, Chan VEY, Tan RT. Convolutional neural networks for automated fracture detection and localization on wrist radiographs. Radiol Artif Intell [Internet]. 2019;1:e180001. https:\/\/doi.org\/10.1148\/ryai.2019180001.","journal-title":"Radiol Artif Intell [Internet]"},{"key":"2731_CR12","first-page":"23","volume":"320","author":"R Voelker","year":"2018","unstructured":"Voelker R. Diagnosing fractures with AI. JAMA. 2018;320:23.","journal-title":"JAMA"},{"key":"2731_CR13","doi-asserted-by":"crossref","unstructured":"Oliveira E Carmo L, van den Merkhof A, Olczak J, Gordon M, Jutte PC, Jaarsma RL, et al. (2021) An increasing number of convolutional neural networks for fracture recognition and classification in orthopaedics\u202f: are these externally validated and ready for clinical application? Bone Jt Open [Internet]. 2:879\u201385. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/34669518","DOI":"10.1302\/2633-1462.210.BJO-2021-0133"},{"key":"2731_CR14","doi-asserted-by":"crossref","unstructured":"Olczak J, Pavlopoulos J, Prijs J, Ijpma FFA, Doornberg JN, Lundstr\u00f6m C, et al. (2021) Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal. Acta Orthop [Internet]. 2021;92:513\u201325. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/33988081","DOI":"10.1080\/17453674.2021.1918389"},{"key":"2731_CR15","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis [Internet]. 2015;115:211\u201352. https:\/\/doi.org\/10.1007\/s11263-015-0816-y.","journal-title":"Int J Comput Vis [Internet]."},{"key":"2731_CR16","unstructured":"Labelbox: The Leading Training Data Platform [Internet]. 2020 [cited 2022 Jul 18]. Available from: labelbox.com"},{"key":"2731_CR17","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN (2020) IEEE Trans Pattern Anal Mach Intell [Internet]. 2020;42:386\u201397. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/29994331","DOI":"10.1109\/TPAMI.2018.2844175"},{"key":"2731_CR18","doi-asserted-by":"publisher","first-page":"2653","DOI":"10.1097\/CORR.0000000000001318","volume":"478","author":"DWG Langerhuizen","year":"2020","unstructured":"Langerhuizen DWG, Bulstra AEJ, Janssen SJ, Ring D, Kerkhoffs GMMJ, Jaarsma RL, et al. Is deep learning on par with human observers for detection of radiographically visible and occult fractures of the scaphoid? Clin Orthop Relat Res. 2020;478:2653\u20139.","journal-title":"Clin Orthop Relat Res"}],"container-title":["European Journal of Trauma and Emergency Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00068-024-02731-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00068-024-02731-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00068-024-02731-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T11:06:41Z","timestamp":1770721601000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00068-024-02731-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,17]]},"references-count":18,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2731"],"URL":"https:\/\/doi.org\/10.1007\/s00068-024-02731-4","relation":{},"ISSN":["1863-9933","1863-9941"],"issn-type":[{"value":"1863-9933","type":"print"},{"value":"1863-9941","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,17]]},"assertion":[{"value":"19 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Jasper Prijs has received the following personal grants: An amount less than USD 15,000 from the Michael van Vloten Foundation (Rotterdam, The Netherlands), an amount less than USD 10,000 from ZonMw (Den Haag, The Netherlands), an amount less than USD 10,000 from the Prins Bernhard Cultuur Fonds (Amsterdam, The Netherlands) Ruurd Jaarsma is unpaid board member of the Australian Orthopaedic Association (SA\/NT) and the SOS Fracture Alliance Australia.Ruurd Jaarsma is unpaid board member of the Australian Orthopaedic Association (SA\/NT) and the SOS Fracture Alliance Australia.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Ethical approval was granted by the ethics committee (CALHN 13991).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"26"}}