{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:58:35Z","timestamp":1781765915097,"version":"3.54.5"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T00:00:00Z","timestamp":1613606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T00:00:00Z","timestamp":1613606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019M651053"],"award-info":[{"award-number":["2019M651053"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Skeletal Radiol"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s00256-021-03709-8","type":"journal-article","created":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T14:02:14Z","timestamp":1613743334000},"page":"1821-1828","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["A fully automated rib fracture detection system on chest CT images and its impact on radiologist performance"],"prefix":"10.1007","volume":"50","author":[{"given":"Xiang Hong","family":"Meng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di Jia","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin Long","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao Man","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ai E","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,2,18]]},"reference":[{"issue":"1","key":"3709_CR1","doi-asserted-by":"publisher","first-page":"e000133","DOI":"10.1136\/tsaco-2017-000133","volume":"2","author":"C Hamilton","year":"2017","unstructured":"Hamilton C, Barnett L, Trop A, Leininger B, Olson A, Brooks A, et al. Emergency department management of patients with rib fracture based on a clinical practice guideline. Trauma Surg Acute Care Open. 2017;2(1):e000133.","journal-title":"Trauma Surg Acute Care Open"},{"issue":"4","key":"3709_CR2","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1016\/j.surg.2016.10.018","volume":"161","author":"NO Shulzhenko","year":"2017","unstructured":"Shulzhenko NO, Zens TJ, Beems MV, Jung HS, O\u2019Rourke AP, Liepert AE, et al. Number of rib fractures thresholds independently predict worse outcomes in older patients with blunt trauma. Surgery. 2017;161(4):1083\u20139.","journal-title":"Surgery."},{"issue":"1","key":"3709_CR3","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.injury.2014.06.014","volume":"46","author":"S Marasco","year":"2015","unstructured":"Marasco S, Lee G, Summerhayes R, Fitzgerald M, Bailey M. Quality of life after major trauma with multiple rib fractures. Injury. 2015;46(1):61\u20135.","journal-title":"Injury."},{"issue":"8","key":"3709_CR4","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1055\/s-0038-1645887","volume":"66","author":"A Sano","year":"2018","unstructured":"Sano A. Rib radiography versus chest computed tomography in the diagnosis of rib fractures. Thorac Cardiovasc Surg. 2018;66(8):693\u20136.","journal-title":"Thorac Cardiovasc Surg"},{"issue":"5S","key":"3709_CR5","doi-asserted-by":"crossref","first-page":"S227","DOI":"10.1016\/j.jacr.2019.02.019","volume":"16","author":"TS Henry","year":"2019","unstructured":"Expert Panel on Thoracic Imaging, Henry TS, Donnelly EF, Boiselle PM, Crabtree TD, Iannettoni MD, et al. ACR appropriateness Criteria\u00ae rib fractures. J Am Coll Radiol. 2019;16(5S):S227\u201334.","journal-title":"J Am Coll Radiol"},{"issue":"6","key":"3709_CR6","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.jcma.2016.01.006","volume":"79","author":"FC Lin","year":"2016","unstructured":"Lin FC, Li RY, Tung YW, Jeng KC, Tsai SC. Morbidity, mortality, associated injuries, and management of traumatic rib fractures. J Chin Med Assoc. 2016;79(6):329\u201334.","journal-title":"J Chin Med Assoc"},{"issue":"1018","key":"3709_CR7","doi-asserted-by":"publisher","first-page":"e845","DOI":"10.1259\/bjr\/28575455","volume":"85","author":"SH Cho","year":"2012","unstructured":"Cho SH, Sung YM, Kim MS. Missed rib fractures on evaluation of initial chest CT for trauma patients: pattern analysis and diagnostic value of coronal multiplanar reconstruction images with multidetector row CT. Br J Radiol. 2012;85(1018):e845\u201350.","journal-title":"Br J Radiol"},{"issue":"4","key":"3709_CR8","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/s00068-012-0186-x","volume":"38","author":"VG Shelat","year":"2012","unstructured":"Shelat VG, Eileen S, John L, Teo LT, Vijayan A, Chiu MT. Chronic pain and its impact on quality of life following a traumatic rib fracture. Eur J Trauma Emerg Surg. 2012;38(4):451\u20135.","journal-title":"Eur J Trauma Emerg Surg"},{"issue":"3","key":"3709_CR9","doi-asserted-by":"publisher","first-page":"506","DOI":"10.2214\/AJR.19.21117","volume":"213","author":"S Gyftopoulos","year":"2019","unstructured":"Gyftopoulos S, Lin D, Knoll F, Doshi AM, Rodrigues TC, Recht MP. Artificial intelligence in musculoskeletal imaging: current status and future directions. AJR Am J Roentgenol. 2019;213(3):506\u201313.","journal-title":"AJR Am J Roentgenol"},{"issue":"11","key":"3709_CR10","doi-asserted-by":"publisher","first-page":"2482","DOI":"10.1097\/CORR.0000000000000848","volume":"477","author":"DWG Langerhuizen","year":"2019","unstructured":"Langerhuizen DWG, Janssen SJ, Mallee WH, van den Bekerom MPJ, Ring D, Kerkhoffs GMMJ, et al. What are the applications and limitations of artificial intelligence for fracture detection and classification in orthopaedic trauma imaging? A systematic review. Clin Orthop Relat Res. 2019;477(11):2482\u201391.","journal-title":"Clin Orthop Relat Res"},{"issue":"4","key":"3709_CR11","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1007\/s10278-018-0167-7","volume":"32","author":"G Kitamura","year":"2019","unstructured":"Kitamura G, Chung CY, Moore BE. Ankle fracture detection utilizing a convolutional neural network ensemble implemented with a small sample, de novo training, and multiview incorporation. J Digit Imaging. 2019;32(4):672\u20137.","journal-title":"J Digit Imaging"},{"issue":"6","key":"3709_CR12","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, et al. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 2017;88(6):581\u20136.","journal-title":"Acta Orthop"},{"key":"3709_CR13","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.cmpb.2019.02.006","volume":"171","author":"YD Pranata","year":"2019","unstructured":"Pranata YD, Wang KC, Wang JC, Idram I, Lai JY, Liu JW, et al. Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Comput Methods Prog Biomed. 2019;171:27\u201337.","journal-title":"Comput Methods Prog Biomed"},{"key":"3709_CR14","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A. V-net: fully convolutional neural networks for volumetric medical image segmentation; 2016 Fourth International Conference on 3D Vision (3DV). 2016. p. 565\u201371.","DOI":"10.1109\/3DV.2016.79"},{"key":"3709_CR15","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S. Deep residual learning for image recognition\/\/IEEE conference on computer vision & pattern recognition. IEEE Computer Society; 2016.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"7","key":"3709_CR16","doi-asserted-by":"publisher","first-page":"869","DOI":"10.3348\/kjr.2019.0651","volume":"21","author":"QQ Zhou","year":"2020","unstructured":"Zhou QQ, Wang J, Tang W, Hu ZC, Xia ZY, Li XS, et al. Automatic detection and classification of rib fractures on thoracic CT using convolutional neural network: accuracy and feasibility. Korean J Radiol. 2020;21(7):869\u201379.","journal-title":"Korean J Radiol"},{"issue":"7","key":"3709_CR17","doi-asserted-by":"publisher","first-page":"891","DOI":"10.3348\/kjr.2019.0653","volume":"21","author":"T Weikert","year":"2020","unstructured":"Weikert T, Noordtzij LA, Bremerich J, Stieltjes B, Parmar V, Cyriac J, et al. Assessment of a deep learning algorithm for the detection of rib fractures on whole-body trauma computed tomography. Korean J Radiol. 2020;21(7):891\u20139.","journal-title":"Korean J Radiol"},{"issue":"4","key":"3709_CR18","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1080\/17453674.2019.1600125","volume":"90","author":"K Gan","year":"2019","unstructured":"Gan K, Xu D, Lin Y, Shen Y, Zhang T, Hu K, et al. Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Acta Orthop. 2019;90(4):394\u2013400.","journal-title":"Acta Orthop"},{"issue":"2","key":"3709_CR19","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s00256-018-3016-3","volume":"48","author":"T Urakawa","year":"2019","unstructured":"Urakawa T, Tanaka Y, Goto S, Matsuzawa H, Watanabe K, Endo N. Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skelet Radiol. 2019;48(2):239\u201344.","journal-title":"Skelet Radiol"},{"issue":"3","key":"3709_CR20","doi-asserted-by":"publisher","first-page":"237.e1","DOI":"10.1016\/j.crad.2019.10.022","volume":"75","author":"JS Yu","year":"2020","unstructured":"Yu JS, Yu SM, Erdal BS, Demirer M, Gupta V, Bigelow M, et al. Detection and localisation of hip fractures on anteroposterior radiographs with artificial intelligence: proof of concept. Clin Radiol. 2020;75(3):237.e1\u20139.","journal-title":"Clin Radiol"},{"issue":"5","key":"3709_CR21","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.crad.2017.11.015","volume":"73","author":"DH Kim","year":"2018","unstructured":"Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol. 2018;73(5):439\u201345.","journal-title":"Clin Radiol"},{"issue":"4","key":"3709_CR22","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1080\/17453674.2018.1453714","volume":"89","author":"SW Chung","year":"2018","unstructured":"Chung SW, Han SS, Lee JW, Oh KS, Kim NR, Yoon JP, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop. 2018;89(4):468\u201373.","journal-title":"Acta Orthop"},{"key":"3709_CR23","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.compbiomed.2018.05.011","volume":"98","author":"N Tomita","year":"2018","unstructured":"Tomita N, Cheung YY, Hassanpour S. Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med. 2018;98:8\u201315.","journal-title":"Comput Biol Med"},{"issue":"7","key":"3709_CR24","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1007\/s00330-015-3598-2","volume":"25","author":"H Ringl","year":"2015","unstructured":"Ringl H, Lazar M, T\u00f6pker M, Woitek R, Prosch H, Asenbaum U, et al. The ribs unfolded - a CT visualization algorithm for fast detection of rib fractures: effect on sensitivity and specificity in trauma patients. Eur Radiol. 2015;25(7):1865\u201374.","journal-title":"Eur Radiol"},{"key":"3709_CR25","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.ejrad.2018.11.011","volume":"110","author":"A Urbaneja","year":"2019","unstructured":"Urbaneja A, De Verbizier J, Formery AS, Tobon-Gomez C, Nace L, Blum A, et al. Automatic rib cage unfolding with CT cylindrical projection reformat in polytraumatized patients for rib fracture detection and characterization: feasibility and clinical application. Eur J Radiol. 2019;110:121\u20137.","journal-title":"Eur J Radiol"},{"issue":"3","key":"3709_CR26","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1097\/00004728-200405000-00013","volume":"28","author":"H Alkadhi","year":"2004","unstructured":"Alkadhi H, Wildermuth S, Marincek B, Boehm T. Accuracy and time efficiency for the detection of thoracic cage fractures: volume rendering compared with transverse computed tomography images. J Comput Assist Tomogr. 2004;28(3):378\u201385.","journal-title":"J Comput Assist Tomogr"}],"container-title":["Skeletal Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00256-021-03709-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00256-021-03709-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00256-021-03709-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T08:08:01Z","timestamp":1626163681000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00256-021-03709-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,18]]},"references-count":26,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["3709"],"URL":"https:\/\/doi.org\/10.1007\/s00256-021-03709-8","relation":{},"ISSN":["0364-2348","1432-2161"],"issn-type":[{"value":"0364-2348","type":"print"},{"value":"1432-2161","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,18]]},"assertion":[{"value":"29 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional review board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was waived because of the retrospective nature of the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}