{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T13:19:03Z","timestamp":1790169543342,"version":"4.1.0"},"reference-count":28,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T00:00:00Z","timestamp":1633910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>(1) Background: Chest radiography (CXR) is still a key diagnostic component in the emergency department (ED). Correct interpretation is essential since some pathologies require urgent treatment. This study quantifies potential discrepancies in CXR analysis between radiologists and non-radiology physicians in training with ED experience. (2) Methods: Nine differently qualified physicians (three board-certified radiologists [BCR], three radiology residents [RR], and three non-radiology residents involved in ED [NRR]) evaluated a series of 563 posterior-anterior CXR images by quantifying suspicion for four relevant pathologies: pleural effusion, pneumothorax, pneumonia, and pulmonary nodules. Reading results were noted separately for each hemithorax on a Likert scale (0\u20134; 0: no suspicion of pathology, 4: safe existence of pathology) adding up to a total of 40,536 reported pathology suspicions. Interrater reliability\/correlation and Kruskal\u2013Wallis tests were performed for statistical analysis. (3) Results: While interrater reliability was good among radiologists, major discrepancies between radiologists\u2019 and non-radiologists\u2019 reading results could be observed in all pathologies. Highest overall interrater agreement was found for pneumothorax detection and lowest agreement in raising suspicion for malignancy suspicious nodules. Pleural effusion and pneumonia were often suspected with indifferent choices (1\u20133). In terms of pneumothorax detection, all readers mainly decided for a clear option (0 or 4). Interrater reliability was usually higher when evaluating the right hemithorax (all pathologies except pneumothorax). (4) Conclusions: Quantified CXR interrater reliability analysis displays a general uncertainty and strongly depends on medical training. NRR can benefit from radiology reporting in terms of time efficiency and diagnostic accuracy. CXR evaluation of long-time trained ED specialists has not been tested.<\/jats:p>","DOI":"10.3390\/diagnostics11101868","type":"journal-article","created":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T09:16:42Z","timestamp":1633943802000},"page":"1868","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Interpretation of Thoracic Radiography Shows Large Discrepancies Depending on the Qualification of the Physician\u2014Quantitative Evaluation of Interobserver Agreement in a Representative Emergency Department Scenario"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4849-8034","authenticated-orcid":false,"given":"Jan","family":"Rudolph","sequence":"first","affiliation":[{"name":"Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3089-9606","authenticated-orcid":false,"given":"Nicola","family":"Fink","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"},{"name":"Comprehensive Pneumology Center (CPC-M), German Center for Lung Research, Max-Lebsche-Platz 31, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julien","family":"Dinkel","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"},{"name":"Comprehensive Pneumology Center (CPC-M), German Center for Lung Research, Max-Lebsche-Platz 31, 81377 Munich, Germany"},{"name":"Department of Radiology, Asklepios Fachklinik M\u00fcnchen, Robert-Koch-Allee 2, 82131 Gauting, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanessa","family":"Koliogiannis","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Schwarze","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0964-8386","authenticated-orcid":false,"given":"Sophia","family":"Goller","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2669-2001","authenticated-orcid":false,"given":"Bernd","family":"Erber","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Geyer","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6248-5128","authenticated-orcid":false,"given":"Boj Friedrich","family":"Hoppe","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9172-3316","authenticated-orcid":false,"given":"Maximilian","family":"Fischer","sequence":"additional","affiliation":[{"name":"Department of Medicine I, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Najib","family":"Ben Khaled","sequence":"additional","affiliation":[{"name":"Department of Medicine II, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian","family":"J\u00f6rgens","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics and Trauma Surgery, Musculoskeletal University Center Munich (MUM), University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Ricke","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6695-2958","authenticated-orcid":false,"given":"Johannes","family":"Rueckel","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bastian Oliver","family":"Sabel","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1378\/chest.10-1302","article-title":"Interpretation of plain chest roentgenogram","volume":"141","author":"Raoof","year":"2012","journal-title":"Chest"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1111\/acem.12878","article-title":"Diagnosing Acute Heart Failure in the Emergency Department: A Systematic Review and Meta-analysis","volume":"23","author":"Martindale","year":"2016","journal-title":"Acad. Emerg. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1097\/01.JAA.0000580528.33851.0c","article-title":"Updated concepts in the diagnosis and management of community-acquired pneumonia","volume":"32","author":"Hunton","year":"2019","journal-title":"JAAPA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1148\/radiology.195.1.7892479","article-title":"Why chest radiography became routine","volume":"195","author":"Gurney","year":"1995","journal-title":"Radiology"},{"key":"ref_5","first-page":"574","article-title":"Chest radiography in general practice: Indications, diagnostic yield and consequences for patient management","volume":"56","author":"Speets","year":"2006","journal-title":"Br. J. Gen Pract."},{"key":"ref_6","unstructured":"Regunath, H., and Oba, Y. (2021, September 20). Community-Acquired Pneumonia, StatPearls, Available online: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK430749\/."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1378\/chest.104.5.1486","article-title":"The incidence of pleural effusion in a well-defined region. Epidemiologic study in central Bohemia","volume":"104","author":"Marel","year":"1993","journal-title":"Chest"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1136\/thoraxjnl-2014-206577","article-title":"Epidemiology of spontaneous pneumothorax: Gender-related differences","volume":"70","author":"Bobbio","year":"2015","journal-title":"Thorax"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"226","DOI":"10.4103\/atm.ATM_110_19","article-title":"Lung nodules: A comprehensive review on current approach and management","volume":"14","author":"Loverdos","year":"2019","journal-title":"Ann. Thorac. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Henostroza, G., Harris, J.B., Kancheya, N., Nhandu, V., Besa, S., Musopole, R., Kr\u00fc\u00fcner, A., Chileshe, C., Dunn, I.J., and Reid, S.E. (2016). Chest radiograph reading and recording system: Evaluation in frontline clinicians in Zambia. BMC Infect. Dis., 16.","DOI":"10.1186\/s12879-016-1460-z"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"519.e1","DOI":"10.1016\/j.crad.2017.01.008","article-title":"Evaluation of a chest radiograph reading and recording system for tuberculosis in a HIV-positive cohort","volume":"72","author":"Kosack","year":"2017","journal-title":"Clin. Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1148\/radiology.217.2.r00nv14456","article-title":"Measuring performance in chest radiography","volume":"217","author":"Potchen","year":"2000","journal-title":"Radiology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.diii.2018.04.007","article-title":"Radiology residents\u2019 skill level in chest x-ray reading","volume":"99","author":"Fabre","year":"2018","journal-title":"Diagn Interv. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1111\/j.1525-1497.2006.00427.x","article-title":"Competency in chest radiography. A comparison of medical students, residents, and fellows","volume":"21","author":"Eisen","year":"2006","journal-title":"J. Gen Intern. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"541","DOI":"10.4300\/JGME-5-4-18","article-title":"Analyzing and interpreting data from likert-type scales","volume":"5","author":"Sullivan","year":"2013","journal-title":"J. Grad. Med. Educ."},{"key":"ref_16","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1186\/s13054-017-1897-5","article-title":"Thoracic ultrasound for pleural effusion in the intensive care unit: a narrative review from diagnosis to treatment","volume":"21","author":"Brogi","year":"2017","journal-title":"Crit Care"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e574","DOI":"10.1097\/CCM.0000000000004397","article-title":"Artificial Intelligence Algorithm Detecting Lung Infection in Supine Chest Radiographs of Critically Ill Patients With a Diagnostic Accuracy Similar to Board-Certified Radiologists","volume":"48","author":"Rueckel","year":"2020","journal-title":"Crit Care Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P., Irvin, J., Ball, R.L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., and Langlotz, C.P. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002686"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4180949","DOI":"10.1155\/2019\/4180949","article-title":"An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare","volume":"2019","author":"Stephen","year":"2019","journal-title":"J. Healthc Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e191095","DOI":"10.1001\/jamanetworkopen.2019.1095","article-title":"DLAD Development and Evaluation Group, Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs","volume":"2","author":"Hwang","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1148\/radiol.2018180237","article-title":"Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs","volume":"290","author":"Nam","year":"2019","journal-title":"Radiology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1007\/s00330-019-06532-x","article-title":"Deep learning-based detection system for multiclass lesions on chest radiographs: Comparison with observer readings","volume":"30","author":"Park","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1097\/RLI.0000000000000707","article-title":"Impact of confounding thoracic tubes and pleural dehiscence extent on artificial intelligence pneumothorax detection in chest radiographs","volume":"55","author":"Rueckel","year":"2020","journal-title":"Investig. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Taylor, A.G., Mielke, C., and Mongan, J. (2018). Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002697"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5341","DOI":"10.1007\/s00330-019-06130-x","article-title":"Application of deep learning-based computer-aided detection system: Detecting pneumothorax on chest radiograph after biopsy","volume":"29","author":"Park","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rudolph, J., Huemmer, C., Ghesu, F.-C., Mansoor, A., Preuhs, A., Fieselmann, A., Fink, N., Dinkel, J., Koliogiannis, V., and Schwarze, V. (2021). Artificial Intelligence in Chest Radiography Reporting Accuracy\u2014Added Clinical Value in the Emergency Unit Setting Without 24\/7 Radiology Coverage. Investig. Radiol., Epub ahead of print.","DOI":"10.1097\/RLI.0000000000000813"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/10\/1868\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:11:34Z","timestamp":1760166694000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/10\/1868"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,11]]},"references-count":28,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["diagnostics11101868"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics11101868","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,11]]}}}