{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T19:48:07Z","timestamp":1790884087083,"version":"4.1.0"},"reference-count":39,"publisher":"Elsevier BV","issue":"9","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":365,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","em-consulte.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Diagnostic and Interventional Imaging"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1016\/j.diii.2025.03.007","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T17:29:47Z","timestamp":1743787787000},"page":"299-311","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"title":["Efficacy of a deep learning-based software for chest X-ray analysis in an emergency department"],"prefix":"10.1016","volume":"106","author":[{"given":"Sathiyamurthy","family":"Selvam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6831-0054","authenticated-orcid":false,"given":"Olivier","family":"Peyrony","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arben","family":"Elezi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adelia","family":"Braganca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne-Marie","family":"Zagdanski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucie","family":"Biard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica","family":"Assouline","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guillaume","family":"Chassagnon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4036-7723","authenticated-orcid":false,"given":"Guillaume","family":"Mulier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9359-7077","authenticated-orcid":false,"given":"Constance","family":"de Margerie-Mellon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.diii.2025.03.007_bib0001","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.ajem.2012.08.041","article-title":"High discordance of chest x-ray and computed tomography for detection of pulmonary opacities in ED patients: implications for diagnosing pneumonia","volume":"31","author":"Self","year":"2013","journal-title":"Am J Emerg Med"},{"key":"10.1016\/j.diii.2025.03.007_bib0002","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.ajem.2016.12.058","article-title":"Utility of chest CT after a chest X-ray in patients presenting to the ED with non-traumatic thoracic emergencies","volume":"35","author":"Gezer","year":"2017","journal-title":"Am J Emerg Med"},{"key":"10.1016\/j.diii.2025.03.007_bib0003","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1016\/j.jacr.2018.11.011","article-title":"Increasing utilization of chest imaging in US emergency departments from 1994 to 2015","volume":"16","author":"Chung","year":"2019","journal-title":"J Am Coll Radiol"},{"key":"10.1016\/j.diii.2025.03.007_bib0004","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s10140-008-0763-9","article-title":"Accuracy of chest radiograph interpretation by emergency physicians","volume":"16","author":"Al aseri","year":"2009","journal-title":"Emerg Radiol"},{"key":"10.1016\/j.diii.2025.03.007_bib0005","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.ajem.2009.07.011","article-title":"Accuracy of radiographic readings in the emergency department","volume":"29","author":"Petinaux","year":"2011","journal-title":"Am J Emerg Med"},{"key":"10.1016\/j.diii.2025.03.007_bib0006","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1136\/pmj.79.930.214","article-title":"Chest radiographs in the emergency department: is the radiologist really necessary?","volume":"79","author":"Gatt","year":"2003","journal-title":"Postgrad Med J"},{"key":"10.1016\/j.diii.2025.03.007_bib0007","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1016\/j.jacr.2023.06.033","article-title":"Diagnostic imaging utilization in the emergency department: recent trends in volume and radiology work relative value units","volume":"20","author":"Poyiadji","year":"2023","journal-title":"J Am Coll Radiol"},{"key":"10.1016\/j.diii.2025.03.007_bib0008","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.diii.2022.11.007","article-title":"Artificial intelligence: a critical review of applications for lung nodule and lung cancer","volume":"104","author":"de Margerie-Mellon","year":"2023","journal-title":"Diagn Interv Imaging"},{"key":"10.1016\/j.diii.2025.03.007_bib0009","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.diii.2022.07.005","article-title":"Artificial intelligence in emergency radiology: a review of applications and possibilities","volume":"104","author":"Katzman","year":"2023","journal-title":"Diagn Interv Imaging"},{"key":"10.1016\/j.diii.2025.03.007_bib0010","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1111\/1754-9485.13274","article-title":"Chest radiographs and machine learning: past, present and future","volume":"65","author":"Jones","year":"2021","journal-title":"J Med Imaging Radiat Oncol"},{"key":"10.1016\/j.diii.2025.03.007_bib0011","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1007\/s00330-021-07892-z","article-title":"Artificial intelligence in radiology: 100 commercially available products and their scientific evidence","volume":"31","author":"van Leeuwen","year":"2021","journal-title":"Eur Radiol"},{"key":"10.1016\/j.diii.2025.03.007_bib0012","doi-asserted-by":"crossref","first-page":"3837","DOI":"10.1007\/s00330-020-07480-7","article-title":"Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation","volume":"31","author":"Baltruschat","year":"2021","journal-title":"Eur Radiol"},{"key":"10.1016\/j.diii.2025.03.007_bib0013","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1148\/radiol.2021202818","article-title":"Added value of deep learning-based detection system for multiple major findings on chest radiographs: a randomized crossover study","volume":"299","author":"Sung","year":"2021","journal-title":"Radiology"},{"key":"10.1016\/j.diii.2025.03.007_bib0014","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1148\/radiol.2018180921","article-title":"Automated triaging of adult chest radiographs with deep artificial neural networks","volume":"291","author":"Annarumma","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.diii.2025.03.007_bib0015","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.230860","article-title":"Using AI to improve radiologist performance in detection of abnormalities on chest radiographs","volume":"309","author":"Bennani","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.diii.2025.03.007_bib0016","doi-asserted-by":"crossref","first-page":"8241","DOI":"10.1007\/s00330-023-10043-1","article-title":"Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation","volume":"33","author":"Chassagnon","year":"2023","journal-title":"Eur Radiol"},{"key":"10.1016\/j.diii.2025.03.007_bib0017","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.diii.2024.11.003","article-title":"An introductory guide to statistics for the radiologist","volume":"106","author":"O'Shaughnessy","year":"2025","journal-title":"Diagn Interv Imaging"},{"key":"10.1016\/j.diii.2025.03.007_bib0018","doi-asserted-by":"crossref","first-page":"2850","DOI":"10.1002\/sim.6148","article-title":"Recommended tests and confidence intervals for paired binomial proportions","volume":"33","author":"Fagerland","year":"2014","journal-title":"Stat Med"},{"key":"10.1016\/j.diii.2025.03.007_bib0019","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"10.1016\/j.diii.2025.03.007_bib0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.crad.2022.08.129","article-title":"Validation study of machine-learning chest radiograph software in primary and emergency medicine","volume":"78","author":"van Beek","year":"2023","journal-title":"Clin Radiol"},{"key":"10.1016\/j.diii.2025.03.007_bib0021","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1148\/radiol.2019191225","article-title":"Deep learning for chest radiograph diagnosis in the emergency department","volume":"293","author":"Hwang","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.diii.2025.03.007_bib0022","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.diii.2023.10.001","article-title":"\"Grid\": a French national initiative as a product of radiology and industry collaboration","volume":"105","author":"Gong","year":"2024","journal-title":"Diagn Interv Imaging"},{"key":"10.1016\/j.diii.2025.03.007_bib0023","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.diii.2023.09.002","article-title":"Group. DFAS. French community grid for the evaluation of radiological artificial intelligence solutions (DRIM France Artificial Intelligence Initiative)","volume":"105","author":"Guenoun","year":"2024","journal-title":"Diagn Interv Imaging"},{"key":"10.1016\/j.diii.2025.03.007_bib0024","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1056\/NEJMoa1102873","article-title":"Reduced lung-cancer mortality with low-dose computed tomographic screening","volume":"365","author":"Aberle","year":"2011","journal-title":"N Engl J Med"},{"key":"10.1016\/j.diii.2025.03.007_bib0025","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2021.41096","article-title":"An artificial intelligence-based chest X-ray model on human nodule detection accuracy from a multicenter study","volume":"4","author":"Homayounieh","year":"2021","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.diii.2025.03.007_bib0026","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2022.29289","article-title":"Association of artificial intelligence-aided chest radiograph interpretation with reader performance and efficiency","volume":"5","author":"Ahn","year":"2022","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.diii.2025.03.007_bib0027","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1002686","article-title":"Deep learning for chest radiograph diagnosis: a retrospective comparison between tCheXNeXt algorithm and practicing radiologists","volume":"15","author":"Rajpurkar","year":"2018","journal-title":"PLoS Med"},{"key":"10.1016\/j.diii.2025.03.007_bib0028","doi-asserted-by":"crossref","first-page":"E496","DOI":"10.1016\/S2589-7500(21)00106-0","article-title":"Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study","volume":"3","author":"Seah","year":"2021","journal-title":"Lancet Digit Health"},{"issue":"6","key":"10.1016\/j.diii.2025.03.007_bib0029","doi-asserted-by":"crossref","first-page":"E1","DOI":"10.1111\/j.1469-0691.2011.03672.x","article-title":"Guidelines for the management of adult lower respiratory tract infections","volume":"17","author":"Woodhead","year":"2011","journal-title":"Clin Microbiol Infect"},{"key":"10.1016\/j.diii.2025.03.007_bib0030","doi-asserted-by":"crossref","first-page":"1981","DOI":"10.3390\/jcm9061981","article-title":"Clinical validation of a deep learning algorithm for detection of pneumonia on chest radiographs in emergency department patients with acute febrile respiratory illness","volume":"9","author":"Kim","year":"2020","journal-title":"J Clin Med"},{"key":"10.1016\/j.diii.2025.03.007_bib0031","first-page":"398","article-title":"Revisiting signs, strengths and weaknesses of standard chest radiography in patients of acute dyspnea in the emergency department","volume":"4","author":"Cardinale","year":"2012","journal-title":"J Thorac Dis"},{"key":"10.1016\/j.diii.2025.03.007_bib0032","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.221926","article-title":"Deep learning analysis of chest radiographs to triage patients with acute chest pain syndrome","volume":"306","author":"Kolossvary","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.diii.2025.03.007_bib0033","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1016\/j.ccm.2021.08.007","article-title":"Pneumothorax: classification and etiology","volume":"42","author":"Huan","year":"2021","journal-title":"Clin Chest Med"},{"key":"10.1016\/j.diii.2025.03.007_bib0034","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1080\/14760584.2023.2261785","article-title":"Clinical, economic, and humanistic burden of community acquired pneumonia in Europe: a systematic literature review","volume":"22","author":"Tsoumani","year":"2023","journal-title":"Expert Rev Vaccines"},{"key":"10.1016\/j.diii.2025.03.007_bib0035","doi-asserted-by":"crossref","DOI":"10.1183\/16000617.0259-2022","article-title":"Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis","volume":"32","author":"Sugibayashi","year":"2023","journal-title":"Eur Respir Rev"},{"key":"10.1016\/j.diii.2025.03.007_bib0036","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1038\/s41591-024-02850-w","article-title":"Heterogeneity and predictors of the effects of AI assistance on radiologists","volume":"30","author":"Yu","year":"2024","journal-title":"Nat Med"},{"key":"10.1016\/j.diii.2025.03.007_bib0037","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.crad.2004.01.011","article-title":"Inter-observer variation in the interpretation of chest radiographs for pneumonia in community-acquired lower respiratory tract infections","volume":"59","author":"Hopstaken","year":"2004","journal-title":"Clin Radiol"},{"key":"10.1016\/j.diii.2025.03.007_bib0038","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1186\/s12890-021-01768-0","article-title":"Deep learning computer-aided detection system for pneumonia in febrile neutropenia patients: a diagnostic cohort study","volume":"21","author":"Hwang","year":"2021","journal-title":"BMC Pulm Med"},{"key":"10.1016\/j.diii.2025.03.007_bib0039","doi-asserted-by":"crossref","first-page":"104512","DOI":"10.1109\/ACCESS.2022.3210468","article-title":"An accurate and explainable deep learning system improves interobserver agreement in the interpretation of chest radiograph","volume":"10","author":"Pham","year":"2022","journal-title":"IEEE Access"}],"container-title":["Diagnostic and Interventional Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2211568425000671?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2211568425000671?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T11:38:25Z","timestamp":1788262705000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2211568425000671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":39,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["S2211568425000671"],"URL":"https:\/\/doi.org\/10.1016\/j.diii.2025.03.007","relation":{},"ISSN":["2211-5684"],"issn-type":[{"value":"2211-5684","type":"print"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Efficacy of a deep learning-based software for chest X-ray analysis in an emergency department","name":"articletitle","label":"Article Title"},{"value":"Diagnostic and Interventional Imaging","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.diii.2025.03.007","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Soci\u00e9t\u00e9 fran\u00e7aise de radiologie. Published by Elsevier Masson SAS.","name":"copyright","label":"Copyright"}]}}