{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T11:57:12Z","timestamp":1793361432123,"version":"4.5.0"},"reference-count":13,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T00:00:00Z","timestamp":1637798400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T00:00:00Z","timestamp":1637798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP18K12104"],"award-info":[{"award-number":["JP18K12104"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Orthop Surg Res"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Although the automatic diagnosis of fractures using artificial intelligence (AI) has recently been reported to be more accurate than those by orthopedics specialists, big data with at least 1000 images or more are required for deep learning of the convolutional neural network (CNN) to improve diagnostic accuracy. The aim of this study was to develop an AI system capable of diagnosing distal radius fractures with high accuracy even when learning with relatively small data by learning to use bi-planar X-rays images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>VGG16, a learned image recognition model, was used as the CNN. It was modified into a network with two output layers to identify the fractures in plain X-ray images. We augmented 369 plain X-ray anteroposterior images and 360 lateral images of distal radius fractures, as well as 129 anteroposterior images and 125 lateral images of normal wrists to conduct training and diagnostic tests. Similarly, diagnostic tests for fractures of the styloid process of the ulna were conducted using 189 plain X-ray anteroposterior images of fractures and 302 images of the normal styloid process. The distal radius fracture is determined by entering an anteroposterior image of the wrist for testing into the trained AI. If it identifies a fracture, it is diagnosed as the same. However, if the anteroposterior image is determined as normal, the lateral image of the same patient is entered. If a fracture is identified, the final diagnosis is fracture; if the lateral image is identified as normal, the final diagnosis is normal.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The diagnostic accuracy of distal radius fractures and fractures of the styloid process of the ulna were 98.0\u2009\u00b1\u20091.6% and 91.1\u2009\u00b1\u20092.5%, respectively. The areas under the receiver operating characteristic curve were 0.991 {\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009540; 95% confidence interval (CI), 0.984\u20130.999} and 0.956 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009450; 95% CI 0.938\u20130.973).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our method resulted in a good diagnostic rate, even when using a relatively small amount of data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13018-021-02845-0","type":"journal-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T13:32:03Z","timestamp":1638279123000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays"],"prefix":"10.1186","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7770-4634","authenticated-orcid":false,"given":"Kunihiro","family":"Oka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryoya","family":"Shiode","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuichi","family":"Yoshii","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroyuki","family":"Tanaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toru","family":"Iwahashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsuyoshi","family":"Murase","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,25]]},"reference":[{"key":"2845_CR1","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1186\/s12891-020-3097-8","volume":"21","author":"J Rundgren","year":"2020","unstructured":"Rundgren J, Bojan A, Mellstrand Navarro C, Enocson A. Epidemiology, classification, treatment and mortality of distal radius fractures in adults: an observational study of 23,394 fractures from the National Swedish Fracture Register. BMC Musculoskelet Disord. 2020;21:88. https:\/\/doi.org\/10.1186\/s12891-020-3097-8.","journal-title":"BMC Musculoskelet Disord"},{"key":"2845_CR2","doi-asserted-by":"publisher","first-page":"2146","DOI":"10.2106\/JBJS.J.01597","volume":"93","author":"R Arora","year":"2011","unstructured":"Arora R, Lutz M, Deml C, Krappinger D, Haug L, Gabl M. A prospective randomized trial comparing nonoperative treatment with volar locking plate fixation for displaced and unstable distal radial fractures in patients sixty-five years of age and older. J Bone Joint Surg Am. 2011;93:2146\u201353. https:\/\/doi.org\/10.2106\/JBJS.J.01597.","journal-title":"J Bone Joint Surg Am"},{"key":"2845_CR3","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1053\/jhsu.2000.jhsu025a0019","volume":"25","author":"BT Young","year":"2000","unstructured":"Young BT, Rayan GM. Outcome following nonoperative treatment of displaced distal radius fractures in low-demand patients older than 60 years. J Hand Surg Am. 2000;25:19\u201328. https:\/\/doi.org\/10.1053\/jhsu.2000.jhsu025a0019.","journal-title":"J Hand Surg Am"},{"key":"2845_CR4","doi-asserted-by":"publisher","first-page":"5221","DOI":"10.1002\/mp.12480","volume":"44","author":"X Zhou","year":"2017","unstructured":"Zhou X, Takayama R, Wang S, Hara T, Fujita H. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys. 2017;44:5221\u201333. https:\/\/doi.org\/10.1002\/mp.12480.","journal-title":"Med Phys"},{"key":"2845_CR5","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1111\/bjd.16924","volume":"180","author":"Y Fujisawa","year":"2019","unstructured":"Fujisawa Y, Otomo Y, Ogata Y, Nakamura Y, Fujita R, Ishitsuka Y, Watanabe R, Okiyama N, Ohara K, Fujimoto M. Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis. Br J Dermatol. 2019;180:373\u201381. https:\/\/doi.org\/10.1111\/bjd.16924.","journal-title":"Br J Dermatol"},{"key":"2845_CR6","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, Hanel D, Gardner M, Gupta A, Hotchkiss R, Potter H. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A. 2018;115:11591\u20136. https:\/\/doi.org\/10.1073\/pnas.1806905115.","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"6","key":"2845_CR7","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, Sk\u00f6ldenberg O, Gordon M. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 2017;88(6):581\u20136. https:\/\/doi.org\/10.1080\/17453674.2017.1344459.","journal-title":"Acta Orthop"},{"key":"2845_CR8","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. CoRR. arXiv:1409.1556 (2014)."},{"key":"2845_CR9","doi-asserted-by":"publisher","first-page":"410","DOI":"10.2106\/JBJS.O.00565","volume":"98","author":"IP Mayne","year":"2016","unstructured":"Mayne IP, Brydges R, Moktar J, Murnaghan ML. Development and assessment of a distal radial fracture model as a clinical teaching tool. J Bone Joint Surg Am. 2016;98:410\u20136. https:\/\/doi.org\/10.2106\/JBJS.O.00565.","journal-title":"J Bone Joint Surg Am"},{"key":"2845_CR10","unstructured":"Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167 (2015)"},{"issue":"4","key":"2845_CR11","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/s10278-017-9955-8","volume":"30","author":"H Lee","year":"2017","unstructured":"Lee H, Tajmir S, Lee J, Zissen M, Yeshiwas BA, Alkasab TK, Choy G, Do S. Fully automated deep learning system for bone age assessment. J Digit Imaging. 2017;30(4):427\u201341. https:\/\/doi.org\/10.1007\/s10278-017-9955-8.","journal-title":"J Digit Imaging"},{"issue":"1","key":"2845_CR12","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1186\/s13018-020-02002-z","volume":"15","author":"N Maffulli","year":"2020","unstructured":"Maffulli N, Rodriguez HC, Stone IW, Nam A, Song A, Gupta M, Alvarado R, Ramon D, Gupta A. Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol. J Orthop Surg Res. 2020;15(1):478. https:\/\/doi.org\/10.1186\/s13018-020-02002-z.","journal-title":"J Orthop Surg Res"},{"issue":"Suppl 3","key":"2845_CR13","doi-asserted-by":"publisher","first-page":"S63","DOI":"10.1016\/j.injury.2019.08.033","volume":"51","author":"G Kakavas","year":"2020","unstructured":"Kakavas G, Malliaropoulos N, Pruna R, Maffulli N. Artificial intelligence: a tool for sports trauma prediction. Injury. 2020;51(Suppl 3):S63\u20135. https:\/\/doi.org\/10.1016\/j.injury.2019.08.033.","journal-title":"Injury"}],"container-title":["Journal of Orthopaedic Surgery and Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13018-021-02845-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13018-021-02845-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13018-021-02845-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T13:40:20Z","timestamp":1638279620000},"score":1,"resource":{"primary":{"URL":"https:\/\/josr-online.biomedcentral.com\/articles\/10.1186\/s13018-021-02845-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,25]]},"references-count":13,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["2845"],"URL":"https:\/\/doi.org\/10.1186\/s13018-021-02845-0","relation":{},"ISSN":["1749-799X"],"issn-type":[{"value":"1749-799X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,25]]},"assertion":[{"value":"3 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Ethical Review Board of Osaka University Hospital (approval number: 18137). This study was performed in line with the principles of the Helsinki Declaration, revised in 2000.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Written consents for publication were obtained from all study participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"694"}}