{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T22:09:15Z","timestamp":1772143755952,"version":"3.50.1"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Vappu Uusp\u00e4\u00e4 Foundation"},{"DOI":"10.13039\/100008723","name":"Suomen L\u00e4\u00e4ketieteen S\u00e4\u00e4ti\u00f6","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008723","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013871","name":"Suomen K\u00e4sikirurgiyhdistys","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008376","name":"Helsingin ja Uudenmaan Sairaanhoitopiiri","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008376","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of Helsinki including Helsinki University Central Hospital"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"abstract":"<jats:title>Abstract\n<\/jats:title><jats:p>Deep learning algorithms can be used to classify medical images. In distal radius fracture treatment, fracture detection and radiographic assessment of fracture displacement are critical steps. The aim of this study was to use pixel-level annotations of fractures to develop a deep learning model for precise distal radius fracture detection. We randomly divided 3785 consecutive emergency wrist radiograph examinations from six hospitals to a training set (3399 examinations) and test set (386 examinations). The training set was used to develop the deep learning model and the test set to assess its validity. The consensus of three hand surgeons was used as the gold standard for the test set. The area under the ROC curve was 0.97 (CI 0.95\u20130.98) and 0.95 (CI 0.92\u20130.98) for examinations without a cast. Fractures were identified with higher accuracy in the postero-anterior radiographs than in the lateral radiographs. Our deep learning model performed well in our multi-hospital and multi-radiograph system manufacturer settings. Thus, segmentation-based deep learning models may provide additional benefit. Further research is needed with algorithm comparison and external validation.\n<\/jats:p>","DOI":"10.1007\/s10278-022-00741-5","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T14:05:21Z","timestamp":1671631521000},"page":"679-687","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Detecting Distal Radius Fractures Using a Segmentation-Based Deep Learning Model"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1563-1940","authenticated-orcid":false,"given":"Turkka T.","family":"Anttila","sequence":"first","affiliation":[]},{"given":"Teemu V.","family":"Karjalainen","sequence":"additional","affiliation":[]},{"given":"Teemu O.","family":"M\u00e4kel\u00e4","sequence":"additional","affiliation":[]},{"given":"Eero M.","family":"Waris","sequence":"additional","affiliation":[]},{"given":"Nina C.","family":"Lindfors","sequence":"additional","affiliation":[]},{"given":"Miika M.","family":"Leminen","sequence":"additional","affiliation":[]},{"given":"Jorma O.","family":"Ryh\u00e4nen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"issue":"7","key":"741_CR1","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1016\/j.injury.2017.04.047","volume":"48","author":"MSH Beerekamp","year":"2017","unstructured":"Beerekamp MSH, de Muinck Keizer RJO, Schep NWL, Ubbink DT, Panneman MJM, Goslings JC. Epidemiology of extremity fractures in the Netherlands. Injury. 2017 Jul;48(7):1355\u201362.","journal-title":"Injury."},{"issue":"8","key":"741_CR2","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1016\/j.injury.2006.04.130","volume":"37","author":"CM Court-Brown","year":"2006","unstructured":"Court-Brown CM, Caesar B. Epidemiology of adult fractures: a review. Injury. 2006;37(8):691\u20137.","journal-title":"Injury."},{"key":"741_CR3","doi-asserted-by":"crossref","unstructured":"Guly HR. Injuries initially misdiagnosed as sprained wrist (beware the sprained wrist). Vol. 19, Emergengy Medicine Journal. 2002. p. 41\u20132.","DOI":"10.1136\/emj.19.1.41"},{"issue":"7","key":"741_CR4","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1080\/02841850600806340","volume":"47","author":"CJ Wei","year":"2006","unstructured":"Wei CJ, Tsai WC, Tiu CM, Wu HT, Chiou HJ, Chang CY. Systematic analysis of missed extremity fractures in emergency radiology. Acta Radiologica. 2006 Sep 1;47(7):710\u20137.","journal-title":"Acta Radiologica."},{"issue":"4","key":"741_CR5","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/j.rcl.2015.02.013","volume":"53","author":"S Tyson","year":"2015","unstructured":"Tyson S, Hatem SF. Easily missed fractures of the upper extremity. Radiologic Clinics of North America. 2015;53(4):717\u201336.","journal-title":"Radiologic Clinics of North America."},{"issue":"2","key":"741_CR6","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1080\/17453674.2018.1427966","volume":"89","author":"H Sandelin","year":"2018","unstructured":"Sandelin H, Waris E, Hirvensalo E, Vasenius J, Huhtala H, Raatikainen T, et al. Patient injury claims involving fractures of the distal radius. Acta Orthopaedica. 2018 Apr;89(2):240\u20135.","journal-title":"Acta Orthopaedica."},{"issue":"1","key":"741_CR7","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nature Medicine. 2019 Jan;25(1):24\u20139.","journal-title":"Nature Medicine."},{"key":"741_CR8","doi-asserted-by":"crossref","unstructured":"Yang R, Yu Y. Artificial convolutional neural network in object detection and semantic segmentation for medical imaging analysis. Vol. 11, Frontiers in Oncology. Frontiers Media S.A.; 2021.","DOI":"10.3389\/fonc.2021.638182"},{"issue":"6","key":"741_CR9","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. Acta Orthopaedica. 2017 Dec;88(6):581\u20136.","journal-title":"Acta Orthopaedica."},{"issue":"2","key":"741_CR10","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s00256-018-3016-3","volume":"48","author":"T Urakawa","year":"2019","unstructured":"Urakawa T, Tanaka Y, Goto S, Matsuzawa H, Watanabe K, Endo N. Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal Radiology. 2019;48(2):239\u201344.","journal-title":"Skeletal Radiology."},{"issue":"4","key":"741_CR11","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 Orthopaedica. 2019 Jul 4;90(4):394\u2013400.","journal-title":"Acta Orthopaedica."},{"key":"741_CR12","doi-asserted-by":"crossref","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. Radiology: Artificial Intelligence. 2019;1(1):e180001.","DOI":"10.1148\/ryai.2019180001"},{"key":"741_CR13","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Philipp F, Brox T. U-Net: convolutional networks for biomedical image segmentation. MICCAI 2015, Part III, LNCS 9351. 2015;9351(Cvd):234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"741_CR14","doi-asserted-by":"crossref","unstructured":"Zuiderveld K. Contrast limited adaptive histograph equalization. In: Graphic Gems IV. San Diego: Academic Press Professional; 1994. p. 474\u2013485.","DOI":"10.1016\/B978-0-12-336156-1.50061-6"},{"issue":"12","key":"741_CR15","doi-asserted-by":"publisher","first-page":"3035","DOI":"10.1007\/s00586-019-06115-w","volume":"28","author":"Y Pan","year":"2019","unstructured":"Pan Y, Chen Q, Chen T, Wang H, Zhu X, Fang Z, et al. Evaluation of a computer-aided method for measuring the Cobb angle on chest X-rays. European Spine Journal. 2019;28(12):3035\u201343.","journal-title":"European Spine Journal."},{"key":"741_CR16","doi-asserted-by":"crossref","unstructured":"Siddique N, Paheding S, Elkin CP, Devabhaktuni V. U-net and its variants for medical image segmentation: a review of theory and applications. IEEE Access. 2021;82031\u201357.","DOI":"10.1109\/ACCESS.2021.3086020"},{"key":"741_CR17","unstructured":"Chollet F. Keras. GitHub. Retrieved from https:\/\/github.com\/fchollet\/keras; 2015."},{"key":"741_CR18","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: large-scale machine learning on heterogeneous systems. Software available from https:\/\/tensorflow.org.; 2015."},{"key":"741_CR19","doi-asserted-by":"crossref","unstructured":"Di Somma S, Paladino L, Vaughan L, Lalle I, Magrini L, Magnanti M. Overcrowding in emergency department: an international issue. Vol. 10, Internal and Emergency Medicine. Springer-Verlag Italia s.r.l.; 2015. p. 171\u20135.","DOI":"10.1007\/s11739-014-1154-8"},{"issue":"5","key":"741_CR20","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1080\/17453674.2021.1918389","volume":"92","author":"J Olczak","year":"2021","unstructured":"Olczak J, Pavlopoulos J, Prijs J, Ijpma FFA, Doornberg JN, Lundstr\u00f6m C, et al. 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 Orthopaedica. 2021 Oct;92(5):513\u201325.","journal-title":"Acta Orthopaedica."},{"issue":"5","key":"741_CR21","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.crad.2017.11.015","volume":"73","author":"DH Kim","year":"2018","unstructured":"Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clinical Radiology. 2018 May;73(5):439\u201345.","journal-title":"Clinical Radiology."},{"issue":"45","key":"741_CR22","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. Proceedings of the National Academy of Sciences of the United States of America. 2018 Oct;115(45):11591\u20136.","journal-title":"Proceedings of the National Academy of Sciences of the United States of America."}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-022-00741-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-022-00741-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-022-00741-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T19:17:00Z","timestamp":1679685420000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-022-00741-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,21]]},"references-count":22,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["741"],"URL":"https:\/\/doi.org\/10.1007\/s10278-022-00741-5","relation":{},"ISSN":["1618-727X"],"issn-type":[{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,21]]},"assertion":[{"value":"7 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The research committee of Helsinki University Hospital (HUS\/379\/2020\/4) approved the study and waived the need for informed consent. This study was completed in accordance with the principles the Declaration of Helsinki of the World Medical Association.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"Radiographs presented in the manuscript are entirely unidentifiable and analyzed after pseudonymization; for this reason, patient consent was not retrieved.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}