{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T11:10:14Z","timestamp":1791976214967,"version":"4.4.0"},"reference-count":36,"publisher":"Elsevier BV","issue":"8","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Foot and Ankle Surgery"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1016\/j.fas.2022.05.005","type":"journal-article","created":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T02:14:05Z","timestamp":1653531245000},"page":"1259-1265","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":68,"title":["Detection of ankle fractures using deep learning algorithms"],"prefix":"10.1016","volume":"28","author":[{"given":"Soheil","family":"Ashkani-Esfahani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reza","family":"Mojahed Yazdi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohan","family":"Bhimani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gino M.","family":"Kerkhoffs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Maas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher W.","family":"DiGiovanni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bart","family":"Lubberts","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Guss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.fas.2022.05.005_bib1","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.rbo.2017.03.006","article-title":"Reproducibility of the Lauge-Hansen, Danis-Weber, and AO classifications for ankle fractures","volume":"53","author":"Fonseca","year":"2018","journal-title":"Rev Bras Ortop"},{"issue":"7","key":"10.1016\/j.fas.2022.05.005_bib2","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.1007\/s11999-008-0543-6","article-title":"Etiology of ankle osteoarthritis","volume":"467","author":"Valderrabano","year":"2009","journal-title":"Clin Orthop Relat Res"},{"issue":"3","key":"10.1016\/j.fas.2022.05.005_bib3","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1002\/jor.23462","article-title":"Post-traumatic osteoarthritis of the ankle: a distinct clinical entity requiring new research approaches","volume":"35","author":"Delco","year":"2017","journal-title":"J Orthop Res"},{"issue":"1105","key":"10.1016\/j.fas.2022.05.005_bib4","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20180989","article-title":"Extremity CT and ultrasound in the assessment of ankle injuries: occult fractures and ligament injuries","volume":"93","author":"Allen","year":"2020","journal-title":"Br J Radiol"},{"key":"10.1016\/j.fas.2022.05.005_bib5","series-title":"McQueen MM. Rockwood and Green's fractures in adults","author":"Tornetta Iii","year":"2019"},{"issue":"Suppl","key":"10.1016\/j.fas.2022.05.005_bib6","doi-asserted-by":"crossref","first-page":"S21","DOI":"10.1016\/j.afjem.2018.07.007","article-title":"Imaging in trauma in limited-resource settings: a literature review","volume":"9","author":"Willett","year":"2019","journal-title":"Afr J Emerg Med"},{"issue":"1","key":"10.1016\/j.fas.2022.05.005_bib7","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s13049-017-0361-5","article-title":"Diagnostic error in the Emergency Department: follow up of patients with minor trauma in the outpatient clinic","volume":"25","author":"Moonen","year":"2017","journal-title":"Scand J Trauma Resusc Emerg Med"},{"issue":"6","key":"10.1016\/j.fas.2022.05.005_bib8","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1007\/s00256-020-03649-9","article-title":"Morphometric analysis of the incisura fibularis in patients with unstable high ankle sprains","volume":"50","author":"Huysse","year":"2021","journal-title":"Skelet Radiol"},{"issue":"4","key":"10.1016\/j.fas.2022.05.005_bib9","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/17453674.2018.1453714","article-title":"Automated detection and classification of the proximal humerus fracture by using deep learning algorithm","volume":"89","author":"Chung","year":"2018","journal-title":"Acta Orthop"},{"issue":"4","key":"10.1016\/j.fas.2022.05.005_bib10","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1080\/17453674.2019.1600125","article-title":"Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments","volume":"90","author":"Gan","year":"2019","journal-title":"Acta Orthop"},{"issue":"1","key":"10.1016\/j.fas.2022.05.005_bib11","first-page":"103","article-title":"Monitoring of the Achilles tendon healing process: can artificial intelligence be helpful?","volume":"21","author":"Kapi\u0144ski","year":"2019","journal-title":"Acta Bioeng Biomech"},{"issue":"4","key":"10.1016\/j.fas.2022.05.005_bib12","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1007\/s10278-018-0167-7","article-title":"Ankle fracture detection utilizing a convolutional neural network ensemble implemented with a small sample, de novo training, and multiview incorporation","volume":"32","author":"Kitamura","year":"2019","journal-title":"J Digit Imaging"},{"issue":"2","key":"10.1016\/j.fas.2022.05.005_bib13","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s00256-018-3016-3","article-title":"Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network","volume":"48","author":"Urakawa","year":"2019","journal-title":"Skelet Radiol"},{"issue":"2","key":"10.1016\/j.fas.2022.05.005_bib14","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1148\/radiol.2017162326","article-title":"Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks","volume":"284","author":"Lakhani","year":"2017","journal-title":"Radiology"},{"issue":"3","key":"10.1016\/j.fas.2022.05.005_bib15","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int J Comput Vis"},{"issue":"1","key":"10.1016\/j.fas.2022.05.005_bib16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-019-0105-1","article-title":"Deep learning predicts hip fracture using confounding patient and healthcare variables","volume":"2","author":"Badgeley","year":"2019","journal-title":"NPJ Digit Med"},{"issue":"6","key":"10.1016\/j.fas.2022.05.005_bib17","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1080\/17453674.2017.1344459","article-title":"Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms\u2014are they on par with humans for diagnosing fractures?","volume":"88","author":"Olczak","year":"2017","journal-title":"Acta Orthop"},{"issue":"6","key":"10.1016\/j.fas.2022.05.005_bib18","doi-asserted-by":"crossref","first-page":"403","DOI":"10.5435\/00124635-200311000-00004","article-title":"Ankle fractures resulting from rotational injuries","volume":"11","author":"Michelson","year":"2003","journal-title":"JAAOS J Am Acad Orthop Surg"},{"issue":"6","key":"10.1016\/j.fas.2022.05.005_bib19","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1097\/NOR.0b013e3181c0110e","article-title":"Ankle sprains and fractures in adults","volume":"28","author":"Small","year":"2009","journal-title":"Orthop Nurs"},{"issue":"1","key":"10.1016\/j.fas.2022.05.005_bib20","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.rbo.2017.03.006","article-title":"Reproducibility of the Lauge-Hansen, Danis-Weber, and AO classifications for ankle fractures","volume":"53","author":"Fonseca","year":"2018","journal-title":"Rev Bras De Ortop"},{"key":"10.1016\/j.fas.2022.05.005_bib21","unstructured":"Murphy E., Gregson C., Von Arx O., Whitehouse M., Budd C., Gill H. ARCHi: Automated recognition and classification of hip fractures; 2018."},{"key":"10.1016\/j.fas.2022.05.005_bib22","unstructured":"Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:150203167; 2015."},{"issue":"5","key":"10.1016\/j.fas.2022.05.005_bib23","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.crad.2017.11.015","article-title":"Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks","volume":"73","author":"Kim","year":"2018","journal-title":"Clin Radiol"},{"key":"10.1016\/j.fas.2022.05.005_bib24","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"Proc IEEE Conf Comput Vis pattern Recognit"},{"key":"10.1016\/j.fas.2022.05.005_bib25","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016","journal-title":"Proc IEEE Conf Comput Vis pattern Recognit"},{"issue":"5","key":"10.1016\/j.fas.2022.05.005_bib26","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.fas.2022.05.005_bib27","unstructured":"Hinton G, Srivastava N, Swersky K. Neural networks for machine learning lecture 6a overview of mini-batch gradient descent. Cited on; 2012:14(8)."},{"key":"10.1016\/j.fas.2022.05.005_bib28","article-title":"Large-margin softmax loss for convolutional neural networks","year":"2016"},{"key":"10.1016\/j.fas.2022.05.005_bib29","series-title":"Advances in neural information processing systems","article-title":"Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping","year":"2001"},{"issue":"3","key":"10.1016\/j.fas.2022.05.005_bib30","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1007\/s00198-016-3828-8","article-title":"Clinical fracture risk evaluated by hierarchical agglomerative clustering","volume":"28","author":"Kruse","year":"2017","journal-title":"Osteoporos Int"},{"issue":"11","key":"10.1016\/j.fas.2022.05.005_bib31","doi-asserted-by":"crossref","first-page":"2482","DOI":"10.1097\/CORR.0000000000000848","article-title":"What Are the applications and limitations of artificial intelligence for fracture detection and classification in orthopaedic trauma imaging? A systematic review","volume":"477","author":"Langerhuizen","year":"2019","journal-title":"Clin Orthop Relat Research\u00ae"},{"issue":"4","key":"10.1016\/j.fas.2022.05.005_bib32","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1007\/s10278-018-0167-7","article-title":"Ankle fracture detection utilizing a convolutional neural network ensemble implemented with a small sample, de novo training, and multiview incorporation","volume":"32","author":"Kitamura","year":"2019","journal-title":"J Digit Imaging"},{"issue":"5","key":"10.1016\/j.fas.2022.05.005_bib33","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.crad.2017.11.015","article-title":"Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks","volume":"73","author":"Kim","year":"2018","journal-title":"Clin Radiol"},{"issue":"4","key":"10.1016\/j.fas.2022.05.005_bib34","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/17453674.2018.1453714","article-title":"Automated detection and classification of the proximal humerus fracture by using deep learning algorithm","volume":"89","author":"Chung","year":"2018","journal-title":"Acta Orthop"},{"issue":"2","key":"10.1016\/j.fas.2022.05.005_bib35","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s00256-018-3016-3","article-title":"Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network","volume":"48","author":"Urakawa","year":"2019","journal-title":"Skelet Radiol"},{"issue":"12","key":"10.1016\/j.fas.2022.05.005_bib36","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1007\/s11548-018-1727-5","article-title":"Reliability and correlation analysis of computed methods to convert conventional 2D radiological hindfoot measurements to a 3D setting using weightbearing CT","volume":"13","author":"Burssens","year":"2018","journal-title":"Int J Comput Assist Radiol Surg"}],"container-title":["Foot and Ankle Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1268773122001023?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1268773122001023?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T11:10:38Z","timestamp":1761131438000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1268773122001023"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12]]},"references-count":36,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["S1268773122001023"],"URL":"https:\/\/doi.org\/10.1016\/j.fas.2022.05.005","relation":{},"ISSN":["1268-7731"],"issn-type":[{"value":"1268-7731","type":"print"}],"subject":[],"published":{"date-parts":[[2022,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Detection of ankle fractures using deep learning algorithms","name":"articletitle","label":"Article Title"},{"value":"Foot and Ankle Surgery","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.fas.2022.05.005","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 European Foot and Ankle Society. Published by Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}]}}