{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T01:55:38Z","timestamp":1781661338807,"version":"3.54.5"},"reference-count":44,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yonsei University College of Medicine","award":["6-2022-0083"],"award-info":[{"award-number":["6-2022-0083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>The prediction of corticosteroid responses in coronavirus disease 2019 (COVID-19) patients is crucial in clinical practice, and exploring the role of artificial intelligence (AI)-assisted analysis of chest radiographs (CXR) is warranted. This retrospective case\u2013control study involving mild-to-moderate COVID-19 patients treated with corticosteroids was conducted from 4 September 2021, to 30 August 2022. The primary endpoint of the study was corticosteroid responsiveness, defined as the advancement of two or more of the eight-categories-ordinal scale. Serial abnormality scores for consolidation and pleural effusion on CXR were obtained using a commercial AI-based software based on days from the onset of symptoms. Amongst the 258 participants included in the analysis, 147 (57%) were male. Multivariable logistic regression analysis revealed that high pleural effusion score at 6\u20139 days from onset of symptoms (adjusted odds ratio of (aOR): 1.022, 95% confidence interval (CI): 1.003\u20131.042, p = 0.020) and consolidation scores up to 9 days from onset of symptoms (0\u20132 days: aOR: 1.025, 95% CI: 1.006\u20131.045, p = 0.010; 3\u20135 days: aOR: 1.03 95% CI: 1.011\u20131.051, p = 0.002; 6\u20139 days: aOR; 1.052, 95% CI: 1.015\u20131.089, p = 0.005) were associated with an unfavorable corticosteroid response. AI-generated scores could help intervene in the use of corticosteroids in COVID-19 patients who would not benefit from them.<\/jats:p>","DOI":"10.3390\/jcm12185852","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T07:57:17Z","timestamp":1694159837000},"page":"5852","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Novel Risks of Unfavorable Corticosteroid Response in Patients with Mild-to-Moderate COVID-19 Identified Using Artificial Intelligence-Assisted Analysis of Chest Radiographs"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0250-212X","authenticated-orcid":false,"given":"Min Hyung","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Internal Medicine, Division of Infectious Disease, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7462-2609","authenticated-orcid":false,"given":"Hyun Joo","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea"},{"name":"Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5706-0181","authenticated-orcid":false,"given":"Jaewoong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Hospital Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea"},{"name":"Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3747-9444","authenticated-orcid":false,"given":"Sunhee","family":"Jo","sequence":"additional","affiliation":[{"name":"Department of Hospital Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3368-5013","authenticated-orcid":false,"given":"Eun-Kyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea"},{"name":"Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoon Soo","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Division of Infectious Disease, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5846-7808","authenticated-orcid":false,"given":"Taeyoung","family":"Kyong","sequence":"additional","affiliation":[{"name":"Department of Hospital Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"ref_1","unstructured":"(2023, June 15). 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