{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T13:11:41Z","timestamp":1789391501058,"version":"build-2803163510"},"reference-count":29,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalimaging.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Clinical Imaging"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1016\/j.clinimag.2021.09.010","type":"journal-article","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T23:55:29Z","timestamp":1632268529000},"page":"24-32","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":31,"special_numbering":"C","title":["Precise anatomical localization and classification of rib fractures on CT using a convolutional neural network"],"prefix":"10.1016","volume":"81","author":[{"given":"Qing-Qing","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhang-Chun","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi-Yi","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiashuo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongguo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen-Yu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingquan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.clinimag.2021.09.010_bb0005","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.jcma.2016.01.006","article-title":"Morbidity, mortality, associated injuries, and management of traumatic rib fractures","volume":"79","author":"Lin","year":"2016","journal-title":"J Chin Med Assoc"},{"issue":"1018","key":"10.1016\/j.clinimag.2021.09.010_bb0010","doi-asserted-by":"crossref","first-page":"e845","DOI":"10.1259\/bjr\/28575455","article-title":"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","volume":"85","author":"Cho","year":"2012","journal-title":"Br J Radiol"},{"issue":"1","key":"10.1016\/j.clinimag.2021.09.010_bb0015","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.ccc.2016.08.009","article-title":"Rib fracture fixation: indications and outcomes","volume":"33","author":"Senekjian","year":"2017","journal-title":"Crit Care Clin"},{"issue":"1118","key":"10.1016\/j.clinimag.2021.09.010_bb0020","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20200870","article-title":"Improving rib fracture detection accuracy and reading efficiency with deep learning-based detection software: a clinical evaluation","volume":"94","author":"Zhang","year":"2020","journal-title":"Br J Radiol"},{"issue":"2","key":"10.1016\/j.clinimag.2021.09.010_bb0025","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1148\/rg.2017160100","article-title":"Traumatic rib injury: patterns, imaging pitfalls, complications, and treatment","volume":"37","author":"Talbot","year":"2017","journal-title":"Radiographics"},{"issue":"22","key":"10.1016\/j.clinimag.2021.09.010_bb0030","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000000889","article-title":"MDCT evaluation of costal bone lesions","volume":"94","author":"Wang","year":"2015","journal-title":"Medicine"},{"issue":"6","key":"10.1016\/j.clinimag.2021.09.010_bb0035","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1016\/j.annemergmed.2017.04.011","article-title":"Rib fracture diagnosis in the Panscan Era","volume":"70","author":"Murphy","year":"2017","journal-title":"Ann Emerg Med"},{"issue":"3","key":"10.1016\/j.clinimag.2021.09.010_bb0040","doi-asserted-by":"crossref","first-page":"196","DOI":"10.4258\/hir.2013.19.3.196","article-title":"Quantitative measurement method for possible rib fractures in chest radiographs","volume":"19","author":"Kim","year":"2013","journal-title":"Healthc Inform Res"},{"issue":"7","key":"10.1016\/j.clinimag.2021.09.010_bb0045","doi-asserted-by":"crossref","first-page":"869","DOI":"10.3348\/kjr.2019.0651","article-title":"Automatic detection and classification of rib fractures on thoracic CT using convolutional neural network: accuracy and feasibility","volume":"21","author":"Zhou","year":"2020","journal-title":"Korean J Radiol"},{"issue":"9","key":"10.1016\/j.clinimag.2021.09.010_bb0050","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1016\/j.jacr.2010.03.004","article-title":"Long radiology workdays reduce detection and accommodation accuracy","volume":"7","author":"Krupinski","year":"2010","journal-title":"J Am Coll Radiol"},{"key":"10.1016\/j.clinimag.2021.09.010_bb0055","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.ejrad.2018.11.011","article-title":"Automatic rib cage unfolding with CT cylindrical projection reformat in polytraumatized patients for rib fracture detection and characterization: feasibility and clinical application","volume":"110","author":"Urbaneja","year":"2019","journal-title":"Eur J Radiol"},{"issue":"7","key":"10.1016\/j.clinimag.2021.09.010_bb0060","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1007\/s00330-015-3598-2","article-title":"The ribs unfolded - a CT visualization algorithm for fast detection of rib fractures: effect on sensitivity and specificity in trauma patients","volume":"25","author":"Ringl","year":"2015","journal-title":"Eur Radiol"},{"issue":"2","key":"10.1016\/j.clinimag.2021.09.010_bb0065","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.acra.2016.09.022","article-title":"Evaluation of rib fractures on a single-in-plane image reformation of the rib cage in CT examinations","volume":"24","author":"Dankerl","year":"2017","journal-title":"Acad Radiol"},{"issue":"2","key":"10.1016\/j.clinimag.2021.09.010_bb0070","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1007\/s00414-016-1412-6","article-title":"New bone post-processing tools in forensic imaging: a multi-reader feasibility study to evaluate detection time and diagnostic accuracy in rib fracture assessment","volume":"131","author":"Glemser","year":"2017","journal-title":"Int J Leg Med"},{"issue":"7","key":"10.1016\/j.clinimag.2021.09.010_bb0075","doi-asserted-by":"crossref","first-page":"891","DOI":"10.3348\/kjr.2019.0653","article-title":"Assessment of a deep learning algorithm for the detection of rib fractures on whole-body trauma computed tomography","volume":"21","author":"Weikert","year":"2020","journal-title":"Korean J Radiol"},{"key":"10.1016\/j.clinimag.2021.09.010_bb0080","first-page":"62","article-title":"Deep-learning-assisted detection and segmentation of rib fractures from CT scans: development and validation of FracNet","author":"Jin","year":"2020","journal-title":"EBioMedicine"},{"issue":"6","key":"10.1016\/j.clinimag.2021.09.010_bb0085","doi-asserted-by":"crossref","first-page":"3815","DOI":"10.1007\/s00330-020-07418-z","article-title":"Automatic detection and classification of rib fractures based on patients' CT images and clinical information via convolutional neural network","volume":"31","author":"Zhou","year":"2020","journal-title":"Eur Radiol"},{"key":"10.1016\/j.clinimag.2021.09.010_bb0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2020.101843","article-title":"Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images","volume":"88","author":"Iqbal","year":"2021","journal-title":"Comput Med Imaging Graph"},{"issue":"2","key":"10.1016\/j.clinimag.2021.09.010_bb0095","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1148\/radiol.2018181422","article-title":"Assessment of convolutional neural networks for automated classification of chest radiographs","volume":"290","author":"Dunnmon","year":"2019","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.clinimag.2021.09.010_bb0100","article-title":"Convolutional neural networks for automated fracture detection and localization on wrist radiographs","volume":"1","author":"Thian","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.clinimag.2021.09.010_bb0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2020.101721","article-title":"Automated coronary artery atherosclerosis detection and weakly supervised localization on coronary CT angiography with a deep 3-dimensional convolutional neural network","volume":"83","author":"Candemir","year":"2020","journal-title":"Comput Med Imaging Graph"},{"issue":"5","key":"10.1016\/j.clinimag.2021.09.010_bb0110","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1097\/BOT.0000000000000778","article-title":"When is a fracture not \u201cFresh\u201d? Aligning reimbursement with patient outcome after treatment with low-intensity pulsed ultrasound","volume":"31","author":"Zura","year":"2017","journal-title":"J Orthop Trauma"},{"issue":"6","key":"10.1016\/j.clinimag.2021.09.010_bb0115","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/j.chiabu.2007.06.011","article-title":"Comparison of computed tomography and chest radiography in the detection of rib fractures in abused infants","volume":"32","author":"Wootton-Gorges","year":"2008","journal-title":"Child Abuse Negl"},{"issue":"2","key":"10.1016\/j.clinimag.2021.09.010_bb0120","first-page":"63","article-title":"Treatment of traumatic clavicular pseudoarthrosis with the hunec colchero nail","volume":"21","author":"Arredondo-Gomez","year":"2007","journal-title":"Acta Ortop Mex"},{"issue":"2","key":"10.1016\/j.clinimag.2021.09.010_bb0125","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal loss for dense object detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10.1016\/j.clinimag.2021.09.010_bb0130","series-title":"Lung infection quantification of COVID-19 in CT images with deep learning","author":"Shan","year":"2020"},{"issue":"4","key":"10.1016\/j.clinimag.2021.09.010_bb0135","doi-asserted-by":"crossref","first-page":"2332","DOI":"10.1007\/s00330-020-07310-w","article-title":"Missed rib fractures on initial chest CT in trauma patients: time patterns, clinical and forensic significance","volume":"31","author":"Hu","year":"2020","journal-title":"Eur Radiol"},{"key":"10.1016\/j.clinimag.2021.09.010_bb0140","doi-asserted-by":"crossref","DOI":"10.1109\/TIP.2019.2917781","article-title":"Feature pyramid reconfiguration with consistent loss for object detection","author":"Sun","year":"2019","journal-title":"IEEE Trans Image Process"},{"issue":"7","key":"10.1016\/j.clinimag.2021.09.010_bb0145","doi-asserted-by":"crossref","first-page":"2937","DOI":"10.1002\/mp.14118","article-title":"Inspection of visible components in urine based on deep learning","volume":"47","author":"Li","year":"2020","journal-title":"Med Phys"}],"container-title":["Clinical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S089970712100382X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S089970712100382X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T11:47:22Z","timestamp":1760269642000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S089970712100382X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":29,"alternative-id":["S089970712100382X"],"URL":"https:\/\/doi.org\/10.1016\/j.clinimag.2021.09.010","relation":{},"ISSN":["0899-7071"],"issn-type":[{"value":"0899-7071","type":"print"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Precise anatomical localization and classification of rib fractures on CT using a convolutional neural network","name":"articletitle","label":"Article Title"},{"value":"Clinical Imaging","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.clinimag.2021.09.010","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}