{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T12:27:36Z","timestamp":1792499256078,"version":"4.5.0"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T00:00:00Z","timestamp":1710806400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T00:00:00Z","timestamp":1710806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["International Orthopaedics (SICOT)"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s00264-024-06125-4","type":"journal-article","created":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T06:12:20Z","timestamp":1710828740000},"page":"1303-1311","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Development of a diagnostic support system for distal humerus fracture using artificial intelligence"],"prefix":"10.1007","volume":"48","author":[{"given":"Aashay","family":"Kekatpure","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aditya","family":"Kekatpure","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjay","family":"Deshpande","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandeep","family":"Srivastava","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,19]]},"reference":[{"key":"6125_CR1","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1007\/s00264-023-05722-z","volume":"47","author":"GM Schwarz","year":"2023","unstructured":"Schwarz GM, Simon S, Mitterer JA et al (2023) Can an artificial intelligence powered software reliably assess pelvic radiographs? Int Orthop 47:945\u2013953. https:\/\/doi.org\/10.1007\/s00264-023-05722-z","journal-title":"Int Orthop"},{"key":"6125_CR2","doi-asserted-by":"publisher","first-page":"653","DOI":"10.5606\/ehc.2020.75300","volume":"31","author":"S Beyaz","year":"2020","unstructured":"Beyaz S (2020) A brief history of artificial intelligence and robotic surgery in orthopedics & traumatology and future expectations. Jt Dis Relat Surg 31:653\u2013655. https:\/\/doi.org\/10.5606\/ehc.2020.75300","journal-title":"Jt Dis Relat Surg"},{"key":"6125_CR3","doi-asserted-by":"publisher","unstructured":"Cesario A, D\u2019Oria M, Calvani R, Picca A, Pietragalla A, Lorusso D, Daniele G, Lohmeyer FM, Boldrini L, Valentini V, Bernabei R (2021) The role of artificial intelligence in managing multimorbidity and cancer. J Personal Med 11(4):314. https:\/\/doi.org\/10.3390\/jpm11040314","DOI":"10.3390\/jpm11040314"},{"issue":"6221","key":"6125_CR4","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1126\/science.347.6221.465","volume":"347","author":"J You","year":"2015","unstructured":"You J (2015) Artificial intelligence. DARPA sets out to automate research. Science 347(6221):465. https:\/\/doi.org\/10.1126\/science.347.6221.465","journal-title":"Science"},{"key":"6125_CR5","doi-asserted-by":"publisher","unstructured":"( 2016)Step aside, Olympics: here\u2019s the Cybathlon. Nature 536(7614):5. https:\/\/doi.org\/10.1038\/536005b","DOI":"10.1038\/536005b"},{"key":"6125_CR6","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 et al (2017) Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms\u2014are they on par with humans for diagnosing fractures? Acta Orthop 88:581\u2013586. https:\/\/doi.org\/10.1080\/17453674.2017.1344459","journal-title":"Acta Orthop"},{"key":"6125_CR7","doi-asserted-by":"publisher","first-page":"e22550","DOI":"10.2196\/22550","volume":"22","author":"YA Almog","year":"2020","unstructured":"Almog YA, Rai A, Zhang P et al (2020) Deep learning with electronic health records for short-term fracture risk identification: crystal bone algorithm development and validation. J Med Internet Res 22:e22550. https:\/\/doi.org\/10.2196\/22550","journal-title":"J Med Internet Res"},{"key":"6125_CR8","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s13755-021-00163-7","volume":"9","author":"I Kandel","year":"2021","unstructured":"Kandel I, Castelli M (2021) Improving convolutional neural networks performance for image classification using test time augmentation: a case study using MURA dataset. Health Inf Sci Syst 9:33. https:\/\/doi.org\/10.1007\/s13755-021-00163-7","journal-title":"Health Inf Sci Syst"},{"key":"6125_CR9","doi-asserted-by":"publisher","first-page":"116017","DOI":"10.1016\/j.bone.2021.116017","volume":"150","author":"BA Monchka","year":"2021","unstructured":"Monchka BA, Kimelman D, Lix LM, Leslie WD (2021) Feasibility of a generalized convolutional neural network for automated identification of vertebral compression fractures: the Manitoba Bone Mineral Density Registry. Bone 150:116017. https:\/\/doi.org\/10.1016\/j.bone.2021.116017","journal-title":"Bone"},{"key":"6125_CR10","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1016\/j.injury.2020.09.010","volume":"52","author":"N Aghnia Farda","year":"2021","unstructured":"Aghnia Farda N, Lai J-Y, Wang J-C et al (2021) Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques. Injury 52:616\u2013624. https:\/\/doi.org\/10.1016\/j.injury.2020.09.010","journal-title":"Injury"},{"key":"6125_CR11","doi-asserted-by":"publisher","first-page":"713.e17","DOI":"10.1016\/j.crad.2020.05.021","volume":"75","author":"S Yang","year":"2020","unstructured":"Yang S, Yin B, Cao W et al (2020) Diagnostic accuracy of deep learning in orthopaedic fractures: a systematic review and meta-analysis. Clin Radiol 75:713.e17-713.e28. https:\/\/doi.org\/10.1016\/j.crad.2020.05.021","journal-title":"Clin Radiol"},{"key":"6125_CR12","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1007\/s11548-019-01934-0","volume":"14","author":"AZ Alsinan","year":"2019","unstructured":"Alsinan AZ, Patel VM, Hacihaliloglu I (2019) Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN. Int J Comput Assist Radiol Surg 14:775\u2013783. https:\/\/doi.org\/10.1007\/s11548-019-01934-0","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"4","key":"6125_CR13","doi-asserted-by":"publisher","first-page":"287","DOI":"10.2174\/1381612827666211213143357","volume":"28","author":"Q Zhou","year":"2022","unstructured":"Zhou Q, Zhu W, Li F et al (2022) Transfer learning of the ResNet-18 and DenseNet-121 model used to diagnose intracranial hemorrhage in CT scanning. Curr Pharm Des 28(4):287\u2013295. https:\/\/doi.org\/10.2174\/1381612827666211213143357","journal-title":"Curr Pharm Des"},{"key":"6125_CR14","doi-asserted-by":"publisher","first-page":"e398","DOI":"10.7717\/peerj-cs.398","volume":"7","author":"N T\u00f6tsch","year":"2021","unstructured":"T\u00f6tsch N, Hoffmann D (2021) Classifier uncertainty: evidence, potential impact, and probabilistic treatment. PeerJ Comput Sci 7:e398. https:\/\/doi.org\/10.7717\/peerj-cs.398","journal-title":"PeerJ Comput Sci"},{"key":"6125_CR15","doi-asserted-by":"publisher","first-page":"e351","DOI":"10.1016\/S2589-7500(22)00004-8","volume":"4","author":"L Oakden-Rayner","year":"2022","unstructured":"Oakden-Rayner L, Gale W, Bonham TA et al (2022) Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy study. Lancet Digit Health 4:e351\u2013e358. https:\/\/doi.org\/10.1016\/S2589-7500(22)00004-8","journal-title":"Lancet Digit Health"},{"issue":"2","key":"6125_CR16","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/s00256-021-03739-2","volume":"51","author":"M Ren","year":"2022","unstructured":"Ren M, Yi PH (2022) Deep learning detection of subtle fractures using staged algorithms to mimic radiologist search pattern. Skeletal Radiol 51(2):345\u2013353. https:\/\/doi.org\/10.1007\/s00256-021-03739-2","journal-title":"Skeletal Radiol"},{"key":"6125_CR17","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1097\/RLI.0000000000000615","volume":"55","author":"JW Choi","year":"2020","unstructured":"Choi JW, Cho YJ, Lee S et al (2020) Using a dual-input convolutional neural network for automated detection of pediatric supracondylar fracture on conventional radiography. Invest Radiol 55:101\u2013110. https:\/\/doi.org\/10.1097\/RLI.0000000000000615","journal-title":"Invest Radiol"},{"issue":"4","key":"6125_CR18","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/s00264-013-2199-4","volume":"38","author":"M Ada\u015f","year":"2014","unstructured":"Ada\u015f M, Bayraktar MK, Tonbul M et al (2014) The role of simple elbow dislocations in cubitus valgus development in children. Int Orthop 38(4):797\u2013802. https:\/\/doi.org\/10.1007\/s00264-013-2199-4","journal-title":"Int Orthop"},{"issue":"6","key":"6125_CR19","doi-asserted-by":"publisher","first-page":"e470","DOI":"10.1097\/BPO.0000000000001817","volume":"41","author":"LC Magee","year":"2021","unstructured":"Magee LC, Baghdadi S, Gohel S, Sankar WN (2021) Complex fracture-dislocations of the elbow in the pediatric population. J Pediatr Orthop 41(6):e470\u2013e474. https:\/\/doi.org\/10.1097\/BPO.0000000000001817","journal-title":"J Pediatr Orthop"},{"key":"6125_CR20","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1007\/s00264-022-05346-9","volume":"46","author":"C Nich","year":"2022","unstructured":"Nich C, Behr J, Crenn V et al (2022) Applications of artificial intelligence and machine learning for the hip and knee surgeon: current state and implications for the future. Int Orthop (SICOT) 46:937\u2013944. https:\/\/doi.org\/10.1007\/s00264-022-05346-9","journal-title":"Int Orthop (SICOT)"},{"issue":"1","key":"6125_CR21","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1093\/ckj\/sfaa188","volume":"14","author":"CL Ramspek","year":"2020","unstructured":"Ramspek CL, Jager KJ, Dekker FW (2020) External validation of prognostic models: what, why, how, when and where? Clin Kidney J 14(1):49\u201358. https:\/\/doi.org\/10.1093\/ckj\/sfaa188","journal-title":"Clin Kidney J"}],"container-title":["International Orthopaedics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00264-024-06125-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00264-024-06125-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00264-024-06125-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T03:45:41Z","timestamp":1712634341000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00264-024-06125-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,19]]},"references-count":21,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["6125"],"URL":"https:\/\/doi.org\/10.1007\/s00264-024-06125-4","relation":{},"ISSN":["0341-2695","1432-5195"],"issn-type":[{"value":"0341-2695","type":"print"},{"value":"1432-5195","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,19]]},"assertion":[{"value":"22 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethical approval was obtained from the Institutional Review Board (IRB) of DMIHER.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Yes.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}