{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T12:27:35Z","timestamp":1792499255516,"version":"4.5.0"},"reference-count":19,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T00:00:00Z","timestamp":1665100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Hand Surg Eur Vol"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:p>This study aimed to develop and evaluate a convolutional neural network for identifying scaphoid fractures on radiographs. A dataset of 1918 wrist radiographs (600 patients) was taken from an orthopaedic referral centre between 2010 to 2020. A YOLOv3 and a MobileNetV3 convolutional neural network were trained for scaphoid detection and fracture classification, respectively. The diagnostic performance of the convolutional neural network was compared with the majority decision of four hand surgeons. The convolutional neural network achieved a sensitivity of 82% and specificity of 94%, with an area under the receiver operating characteristic of 92%, whereas the surgeons achieved a sensitivity of 76% and specificity of 96%. The comparison indicated that the convolutional neural network\u2019s performance was similar to the majority vote of surgeons. It further revealed that convolutional neural network could be used in identifying scaphoid fractures on radiographs reliably, and has potential to achieve the expert-level performance.<\/jats:p>\n                  <jats:p>Level of evidence: III<\/jats:p>","DOI":"10.1177\/17531934221127092","type":"journal-article","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T03:38:40Z","timestamp":1665113920000},"page":"445-450","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":36,"title":["Evaluation of a convolutional neural network to identify scaphoid fractures on radiographs"],"prefix":"10.1177","volume":"48","author":[{"given":"Tao","family":"Li","sequence":"first","affiliation":[{"name":"University of Science and Technology Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaobin","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Hand Surgery, Beijing Ji Shui Tan Hospital, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Yi","sequence":"additional","affiliation":[{"name":"Department of Hand Surgery, Beijing Ji Shui Tan Hospital, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Radiology, Beijing Ji Shui Tan Hospital, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenlin","family":"Guo","sequence":"additional","affiliation":[{"name":"Beijing Computational Science Research Center, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3344-333X","authenticated-orcid":false,"given":"Shanlin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Hand Surgery, Beijing Ji Shui Tan Hospital, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,10,7]]},"reference":[{"key":"bibr1-17531934221127092","first-page":"175","author":"Dutta P","year":"2020","journal-title":"ICICT"},{"key":"bibr2-17531934221127092","doi-asserted-by":"publisher","DOI":"10.3390\/app10238667"},{"key":"bibr3-17531934221127092","first-page":"770","author":"He K","year":"2016","journal-title":"IEEE CVPR"},{"key":"bibr4-17531934221127092","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2021200260"},{"key":"bibr5-17531934221127092","first-page":"1314","author":"Howard A","year":"2019","journal-title":"IEEE\/CVF ICCV"},{"key":"bibr6-17531934221127092","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2913372"},{"key":"bibr7-17531934221127092","doi-asserted-by":"publisher","DOI":"10.1097\/CORR.0000000000001318"},{"key":"bibr8-17531934221127092","first-page":"756","author":"Lazuardi RN","year":"2020","journal-title":"IEEE TENCON"},{"key":"bibr9-17531934221127092","doi-asserted-by":"publisher","DOI":"10.1007\/s00068-020-01468-0"},{"key":"bibr10-17531934221127092","first-page":"1742","author":"Papandreou G","year":"2015","journal-title":"IEEE ICCV"},{"key":"bibr11-17531934221127092","first-page":"512","author":"Razavian AS","year":"2014","journal-title":"IEEE CVPRW"},{"key":"bibr12-17531934221127092","unstructured":"Redmon J, Farhadi A. 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