{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T17:58:37Z","timestamp":1781719117424,"version":"3.54.5"},"reference-count":39,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Hubei Provincial Department of Education of China","award":["B2022032"],"award-info":[{"award-number":["B2022032"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    The peculiarities of older individuals related to osteoporosis and hyperostosis may lead to a higher rate of misdiagnosis of rib fractures on computed tomography (CT) images in older than in middle-aged\/young people when using radiologist-only reading. However, none of these studies on rib fracture computer-aided diagnostic (CAD) systems grouped patients by age or evaluated the value of using CAD in older patients. To address these gaps, we divided 1,012 blunt chest trauma emergency patients who underwent chest CT into middle-aged\/young and older groups with a cutoff age of 60 years. CT images were read by six radiologists from three institutions (each with 7 years of experience in thoracic CT diagnosis) using two reading methods, radiologist-only and radiologist-CAD reading, to explore the value of a deep learning (DL)-based CAD system for detecting rib fractures in emergency older patients. The final findings of the independent panel consisting of two senior radiologists with or without a thoracic surgeon were set as the reference standard. The sensitivity was calculated by dividing the number of true positives by the overall number of fractures, as confirmed by an expert panel. The false positives per patient (FPPP) was calculated by dividing the number of false positives by the overall number of patients. Sensitivity and FPPP were used to evaluate the diagnostic efficiency. Sensitivity, FPPP, and reading time were compared between the two groups, as well as reading methods. The results showed the following: (1) Sensitivity for detecting fresh fractures using radiologist-only reading was lower in the older than in the middle-aged\/young group (86.7% [95% confidence interval (CI): 86.3%, 91.7%] vs. 91.5% [95% CI: 89.9%, 92.9%],\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05). With the assistance of the CAD system, the sensitivity increased in the older group to the same level as that in the middle-aged\/young group using radiologist-only reading (92.5% [95% CI: 90.4%, 94.2%] vs. 91.5% [95% CI: 89.9%, 92.9%],\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&gt;\u20090.05). (2) The FPPP of fresh fractures with radiologist-only reading was higher in the older than in the middle-aged\/young group (0.47 vs. 0.37,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05). With the assistance of the CAD system, the FPPP in the older group decreased to the same level as that in the middle-aged\/young group when using radiologist-only or radiologist-CAD reading (0.37 vs. 0.37\/0.39,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&gt;\u20090.05). (3) The reading time of fresh fractures when using radiologist-only reading was longer in the older than in the middle-aged\/young group (6.1 vs 5.4 min,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05). With the assistance of the CAD system, the reading time in the older group was reduced by approximately 36% (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05). We conclude that the efficiency of intermediate-level radiologists in diagnosing fresh rib fractures by radiologist-only reading in older emergency patients was lower than that in middle-aged\/young patients. When a DL-based CAD system assists radiologists, the diagnostic efficiency of identifying fresh fractures in older patients improves to the same level as independent radiologist-only reading in middle-aged\/young patients while reducing the reading time.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0351988","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T17:35:52Z","timestamp":1781717752000},"page":"e0351988","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Computer-aided diagnosis system for thoracic computed tomography of rib fractures in older emergency patients: A preliminary study"],"prefix":"10.1371","volume":"21","author":[{"given":"Shan","family":"Xiong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenze","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sibin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianmin","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4871-1491","authenticated-orcid":true,"given":"Bing","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"issue":"1","key":"pone.0351988.ref001","doi-asserted-by":"crossref","DOI":"10.1136\/tsaco-2020-000441","article-title":"Traumatic rib fractures: A marker of severe injury. A nationwide study using the National Trauma Data Bank","volume":"5","author":"J Peek","year":"2020","journal-title":"Trauma Surg Acute Care Open"},{"issue":"2","key":"pone.0351988.ref002","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":"BS Talbot","year":"2017","journal-title":"Radiographics"},{"issue":"7","key":"pone.0351988.ref003","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":"H Ringl","year":"2015","journal-title":"Eur Radiol"},{"key":"pone.0351988.ref004","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":"A Urbaneja","year":"2019","journal-title":"Eur J Radiol"},{"key":"pone.0351988.ref005","unstructured":"Radiologists RCo. Clinical radiology UK workforce census 2015 report. London: The Royal College of Radiologists. 2016."},{"issue":"11","key":"pone.0351988.ref006","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.2106\/JBJS.J.00160","article-title":"Hounsfield units for assessing bone mineral density and strength: A tool for osteoporosis management","volume":"93","author":"JJ Schreiber","year":"2011","journal-title":"J Bone Joint Surg Am"},{"issue":"3","key":"pone.0351988.ref007","doi-asserted-by":"crossref","first-page":"691","DOI":"10.3390\/diagnostics12030691","article-title":"Machine learning for opportunistic screening for osteoporosis from CT scans of the wrist and forearm","volume":"12","author":"R Sebro","year":"2022","journal-title":"Diagnostics (Basel)"},{"issue":"5","key":"pone.0351988.ref008","doi-asserted-by":"crossref","first-page":"384","DOI":"10.3340\/jkns.2013.54.5.384","article-title":"Correlation between bone mineral density measured by dual-energy X-ray absorptiometry and hounsfield units measured by diagnostic CT in lumbar spine","volume":"54","author":"S Lee","year":"2013","journal-title":"J Korean Neurosurg Soc"},{"issue":"10","key":"pone.0351988.ref009","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.21037\/qims-2020-16","article-title":"Chinese expert consensus on the diagnosis of osteoporosis by imaging and bone mineral density","volume":"10","author":"X Cheng","year":"2020","journal-title":"Quant Imaging Med Surg"},{"issue":"2","key":"pone.0351988.ref010","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s00198-019-05219-9","article-title":"Rib fractures after blunt thoracic trauma in patients with normal versus diminished bone mineral density: A retrospective cohort study","volume":"31","author":"JTH Prins","year":"2020","journal-title":"Osteoporos Int"},{"issue":"2","key":"pone.0351988.ref011","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1002\/jbmr.4477","article-title":"Opportunistic osteoporosis screening using chest radiographs with deep learning: Development and external validation with a cohort dataset","volume":"37","author":"M Jang","year":"2022","journal-title":"J Bone Miner Res"},{"issue":"1","key":"pone.0351988.ref012","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1148\/radiol.12110462","article-title":"Osteoporosis imaging: State of the art and advanced imaging","volume":"263","author":"TM Link","year":"2012","journal-title":"Radiology"},{"issue":"18","key":"pone.0351988.ref013","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.2106\/JBJS.16.00749","article-title":"Opportunistic use of CT imaging for osteoporosis screening and bone density assessment: A qualitative systematic review","volume":"99","author":"EB Gausden","year":"2017","journal-title":"J Bone Joint Surg Am"},{"key":"pone.0351988.ref014","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.media.2018.07.003","article-title":"Measuring rib cortical bone thickness and cross section from CT","volume":"49","author":"SA Holcombe","year":"2018","journal-title":"Med Image Anal"},{"issue":"4","key":"pone.0351988.ref015","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.rcl.2022.02.003","article-title":"Osteoporosis Imaging","volume":"60","author":"D Martel","year":"2022","journal-title":"Radiol Clin North Am"},{"issue":"2","key":"pone.0351988.ref016","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1148\/radiol.2019190434","article-title":"Screening of bone density at CT: An overlooked opportunity","volume":"291","author":"AD Smith","year":"2019","journal-title":"Radiology"},{"key":"pone.0351988.ref017","first-page":"47","article-title":"The incidence of cervical spondylosis decreases with aging in the elderly, and increases with aging in the young and adult population: A hospital-based clinical analysis","volume":"11","author":"C Wang","year":"2016","journal-title":"Clin Interv Aging"},{"key":"pone.0351988.ref018","doi-asserted-by":"crossref","unstructured":"Yuan J, Zhou X, Cheng Q, Wang X, Feng T, Zhan H. Analysis of photoacoustic signals of hyperosteogeny and osteoporosis. Photons Plus Ultrasound: Imaging and Sensing 2019, 2019. 189. https:\/\/doi.org\/10.1117\/12.2506671","DOI":"10.1117\/12.2506671"},{"issue":"1","key":"pone.0351988.ref019","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1148\/radiology.156.1.3923556","article-title":"Degenerative diseases of the vertebral column","volume":"156","author":"D Resnick","year":"1985","journal-title":"Radiology"},{"issue":"10182","key":"pone.0351988.ref020","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1016\/S0140-6736(19)30417-9","article-title":"Osteoarthritis","volume":"393","author":"DJ Hunter","year":"2019","journal-title":"Lancet"},{"key":"pone.0351988.ref021","first-page":"111","article-title":"Traumatic fractures in adults: Missed diagnosis on plain radiographs in the Emergency Department","volume":"89","author":"A Pinto","year":"2018","journal-title":"Acta Biomed"},{"issue":"1","key":"pone.0351988.ref022","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s00414-019-02195-x","article-title":"Automatic rib unfolding in postmortem computed tomography: Diagnostic evaluation of the OpenRib software compared with the autopsy in the detection of rib fractures","volume":"134","author":"M Kolopp","year":"2020","journal-title":"Int J Legal Med"},{"issue":"7","key":"pone.0351988.ref023","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":"T Weikert","year":"2020","journal-title":"Korean J Radiol"},{"key":"pone.0351988.ref024","doi-asserted-by":"crossref","first-page":"103106","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 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of rib fractures via computed tomography","volume":"154","author":"C Yang","year":"2022","journal-title":"Eur J Radiol"},{"issue":"1","key":"pone.0351988.ref031","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1186\/s12880-023-01012-7","article-title":"The value of deep learning-based computer aided diagnostic system in improving diagnostic performance of rib fractures in acute blunt trauma","volume":"23","author":"H Tan","year":"2023","journal-title":"BMC Med Imaging"},{"issue":"1","key":"pone.0351988.ref032","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"EJ Topol","year":"2019","journal-title":"Nat Med"},{"issue":"3","key":"pone.0351988.ref033","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1148\/radiol.210937","article-title":"Improving radiographic fracture recognition performance and efficiency using 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Aligning reimbursement with patient outcome after treatment with low-intensity pulsed ultrasound","volume":"31","author":"R Zura","year":"2017","journal-title":"J Orthop Trauma"},{"issue":"5","key":"pone.0351988.ref035","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.legalmed.2011.05.004","article-title":"\u201cBuckle\u201d rib fracture: An artifact following cardio-pulmonary resuscitation detected on postmortem CT","volume":"13","author":"K-M Yang","year":"2011","journal-title":"Leg Med (Tokyo)"},{"key":"pone.0351988.ref036","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.forsciint.2013.08.025","article-title":"Rib fractures at postmortem computed tomography (PMCT) validated against the autopsy","volume":"233","author":"C Schulze","year":"2013","journal-title":"Forensic Sci Int"},{"issue":"7","key":"pone.0351988.ref037","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":"QQ Zhou","year":"2020","journal-title":"Korean J Radiol"},{"issue":"11","key":"pone.0351988.ref038","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1177\/02841851211043839","article-title":"Clinical evaluation of AI software for rib fracture detection and its impact on junior radiologist performance","volume":"63","author":"X Liu","year":"2022","journal-title":"Acta Radiol"},{"issue":"2","key":"pone.0351988.ref039","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1177\/02841851221083519","article-title":"Comparison and verification of two deep learning models for the detection of chest CT rib fractures","volume":"64","author":"S Hongbiao","year":"2023","journal-title":"Acta Radiol"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0351988","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T17:36:00Z","timestamp":1781717760000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0351988"}},"subtitle":[],"editor":[{"given":"Christopher","family":"Njeh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,6,17]]},"references-count":39,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6,17]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0351988","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,17]]}}}