{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T21:06:08Z","timestamp":1793912768786,"version":"4.8.0"},"reference-count":21,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["The American Journal of Emergency Medicine"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1016\/j.ajem.2023.07.018","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T21:16:21Z","timestamp":1689369381000},"page":"34-38","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":12,"special_numbering":"C","title":["Evaluating AI rib fracture detections using follow-up CT scans"],"prefix":"10.1016","volume":"72","author":[{"given":"Quanshuai","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peixin","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junqi","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiyi","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiqian","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binghui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guojie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.ajem.2023.07.018_bb0005","doi-asserted-by":"crossref","first-page":"23513","DOI":"10.1038\/s41598-021-03002-7","article-title":"Rib fracture detection system based on deep learning","volume":"11","author":"Yao","year":"2021","journal-title":"Sci Rep"},{"issue":"6","key":"10.1016\/j.ajem.2023.07.018_bb0010","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1016\/j.injury.2019.04.020","article-title":"Clinical management of rib fractures and methods for prevention of pulmonary complications: a review","volume":"50","author":"Martin","year":"2019","journal-title":"Injury."},{"issue":"2","key":"10.1016\/j.ajem.2023.07.018_bb0015","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1148\/radiol.12111119","article-title":"The causes of medical malpractice suits against radiologists in the United States","volume":"266","author":"Whang","year":"2013","journal-title":"Radiology."},{"issue":"4","key":"10.1016\/j.ajem.2023.07.018_bb0020","first-page":"905","article-title":"CT diagnosis of rib fractures and the prediction of acute respiratory failure","volume":"64","author":"Livingston","year":"2008","journal-title":"J Trauma"},{"key":"10.1016\/j.ajem.2023.07.018_bb0025","article-title":"Magnetic resonance imaging has better accuracy in detecting new-onset rib fractures as compared to computed tomography","volume":"27","author":"Zhang","year":"2021","journal-title":"Med Sci Monit"},{"key":"10.1016\/j.ajem.2023.07.018_bb0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.forsciint.2019.109942","article-title":"Flat chest projection in the detection and visualization of rib fractures: a cross-sectional study comparing curved and multiplanar reformation of computed tomography images in different reader groups","volume":"303","author":"Mauf","year":"2019","journal-title":"Forensic Sci Int"},{"issue":"2","key":"10.1016\/j.ajem.2023.07.018_bb0035","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":"7","key":"10.1016\/j.ajem.2023.07.018_bb0040","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":"1018","key":"10.1016\/j.ajem.2023.07.018_bb0045","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":"4","key":"10.1016\/j.ajem.2023.07.018_bb0050","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":"2021","journal-title":"Eur Radiol"},{"key":"10.1016\/j.ajem.2023.07.018_bb0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2021.101937","article-title":"Assessing the speed-accuracy trade-offs of popular convolutional neural networks for single-crop rib fracture classification","volume":"91","author":"Castro-Zunti","year":"2021","journal-title":"Comput Med Imaging Graph"},{"key":"10.1016\/j.ajem.2023.07.018_bb0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2020.103106","article-title":"Deep-learning-assisted detection and segmentation of rib fractures from CT scans: development and validation of FracNet","volume":"62","author":"Jin","year":"2020","journal-title":"EBioMedicine."},{"issue":"9","key":"10.1016\/j.ajem.2023.07.018_bb0065","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1007\/s00256-021-03709-8","article-title":"A fully automated rib fracture detection system on chest CT images and its impact on radiologist performance","volume":"50","author":"Meng","year":"2021","journal-title":"Skeletal Radiol"},{"issue":"7","key":"10.1016\/j.ajem.2023.07.018_bb0070","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"},{"issue":"1118","key":"10.1016\/j.ajem.2023.07.018_bb0075","doi-asserted-by":"crossref","first-page":"20200870","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":"2021","journal-title":"Br J Radiol"},{"issue":"6","key":"10.1016\/j.ajem.2023.07.018_bb0080","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\u2019 CT images and clinical information via convolutional neural network","volume":"31","author":"Zhou","year":"2021","journal-title":"Eur Radiol"},{"issue":"2","key":"10.1016\/j.ajem.2023.07.018_bb0085","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1097\/PEC.0000000000000608","article-title":"Characteristics of rib fractures in child abuse-the role of low-dose chest computed tomography","volume":"34","author":"Sanchez","year":"2018","journal-title":"Pediatr Emerg Care"},{"issue":"4","key":"10.1016\/j.ajem.2023.07.018_bb0090","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/S1072-7515(02)01894-X","article-title":"Morbidity from rib fractures increases after age 45","volume":"196","author":"Holcomb","year":"2003","journal-title":"J Am Coll Surg"},{"issue":"4","key":"10.1016\/j.ajem.2023.07.018_bb0095","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":"2021","journal-title":"Eur Radiol"},{"issue":"4","key":"10.1016\/j.ajem.2023.07.018_bb0100","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1007\/s00068-012-0186-x","article-title":"Chronic pain and its impact on quality of life following a traumatic rib fracture","volume":"38","author":"Shelat","year":"2012","journal-title":"Eur J Trauma Emerg Surg"},{"issue":"2","key":"10.1016\/j.ajem.2023.07.018_bb0105","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"}],"container-title":["The American Journal of Emergency Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0735675723003716?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0735675723003716?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:05:59Z","timestamp":1760025959000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0735675723003716"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":21,"alternative-id":["S0735675723003716"],"URL":"https:\/\/doi.org\/10.1016\/j.ajem.2023.07.018","relation":{},"ISSN":["0735-6757"],"issn-type":[{"value":"0735-6757","type":"print"}],"subject":[],"published":{"date-parts":[[2023,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Evaluating AI rib fracture detections using follow-up CT scans","name":"articletitle","label":"Article Title"},{"value":"The American Journal of Emergency Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ajem.2023.07.018","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}