{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T13:30:06Z","timestamp":1791379806881,"version":"4.3.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T00:00:00Z","timestamp":1620000000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T00:00:00Z","timestamp":1620000000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Musculoskelet Disord"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Less experienced clinicians sometimes make misdiagnosis of hip fractures. We developed computer-aided diagnosis (CAD) system for hip fractures on plain X-rays using a deep learning model trained on a large dataset. In this study, we examined whether the accuracy of the diagnosis of hip fracture of the residents could be improved by using this system.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>A deep convolutional neural network approach was used for machine learning. Pytorch 1.3 and Fast.ai 1.0 were applied as frameworks, and an EfficientNet-B4 model (a pre-trained ImageNet model) was used. We handled the 5295 X-rays from the patients with femoral neck fracture or femoral trochanteric fracture from 2009 to 2019. We excluded cases in which the bilateral hips were not included within an image range, and cases of femoral shaft fracture and periprosthetic fracture. Finally, we included 5242 AP pelvic X-rays from 4851 cases. We divided these 5242 images into two images per image, and prepared 5242 images including fracture site and 5242 images without fracture site. Thus, a total of 10,484 images were used for machine learning. The accuracy, sensitivity, specificity, F-value, and area under the curve (AUC) were assessed. Gradient-weighted class activation mapping (Grad-CAM) was used to conceptualize the basis for the diagnosis of the fracture by the deep learning algorithm. Secondly, we conducted a controlled experiment with clinicians. Thirty-one residents;young doctors within 2 years of graduation from medical school who rotate through various specialties, were tested using 300 hip fracture images that were randomly extracted from the dataset. We evaluated the diagnostic accuracy with and without the use of the CAD system for each of the 300 images.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The accuracy, sensitivity, specificity, F-value, and AUC were 96.1, 95.2, 96.9%, 0.961, and 0.99, respectively, with the correct diagnostic basis generated by Grad-CAM. In the controlled experiment, the diagnostic accuracy of the residents significantly improved when they used the CAD system.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>We developed a newly CAD system with a deep learning algorithm from a relatively large dataset from multiple institutions. Our system achieved high diagnostic performance. Our system improved the diagnostic accuracy of residents for hip fractures.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Level of evidence<\/jats:title>\n                <jats:p>Level III, Foundational evidence, before-after study. Clinical relevance: high<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12891-021-04260-2","type":"journal-article","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T14:49:18Z","timestamp":1620053358000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Artificial intelligence improves the accuracy of residents in the diagnosis of hip fractures: a multicenter study"],"prefix":"10.1186","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6144-3735","authenticated-orcid":false,"given":"Yoichi","family":"Sato","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasuhiko","family":"Takegami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takamune","family":"Asamoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yutaro","family":"Ono","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsugeno","family":"Hidetoshi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryosuke","family":"Goto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akira","family":"Kitamura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seiwa","family":"Honda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,3]]},"reference":[{"key":"4260_CR1","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1007\/s00774-009-0080-8","volume":"27","author":"N Yoshimura","year":"2009","unstructured":"Yoshimura N, Muraki S, Oka H, Mabuchi A, En-Yo Y, Yoshida M, et al. 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This study was conducted with the approval of the ethics committee of each hospital (Gamagori City Hospital: approval No. 368-1, Tsushima City Hospital: approval No. 2019-3, Nagoya Daini Red Cross Hospital: approval No. 1360). At each site, the chair of the ethics committee gave administrative permission for access to the raw data.In this study, we adopted an opt-out method for obtaining patient consent, and obtained permission from the ethics committee of each institution. This research falls into the category of \u201cresearch that does not use samples obtained from human subjects\u201d in the \u201cEthical Guidelines for Medical Research Involving Human Subjects\u201d under \u201cresearch using existing samples and information held at the institution\u201d. In accordance with the guidance, we published the disclosure document of this research on each hospital\u2019s website for 30\u2009days from the date of hospital director\u2019s approval, notifying the research subjects and guaranteeing them the opportunity to refuse the research.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors, RG and AK, are employees of Search Space Co. Ltd., a startup company, the eventual products and services of which will be related to the subject matter of the article<b>.<\/b> No authors own shares in the above companies. SH, the last author, represents the AI research division in the nonprofit organization (NPO) Nagoya Orthopedic Regional Healthcare Support Center, (). NPO Nagoya Orthopedic Regional Healthcare Support Center, AI Research Division is a research division established for multi-center collaborative research. With the exception of two Search Space Co. Ltd. employees and one NPO employee, no authors received compensation from these organizations.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"407"}}