{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T01:54:27Z","timestamp":1781661267253,"version":"3.54.5"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T00:00:00Z","timestamp":1686873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002573","name":"Yonsei University College of Medicine","doi-asserted-by":"publisher","award":["6-2022-0083"],"award-info":[{"award-number":["6-2022-0083"]}],"id":[{"id":"10.13039\/501100002573","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>This study aimed to investigate the clinical implications and prognostic value of artificial intelligence (AI)-based results for chest radiographs (CXR) in coronavirus disease 2019 (COVID-19) patients. Patients who were admitted due to COVID-19 from September 2021 to March 2022 were retrospectively included. A commercial AI-based software was used to assess CXR data for consolidation and pleural effusion scores. Clinical data, including laboratory results, were analyzed for possible prognostic factors. Total O2 supply period, the last SpO2 result, and deterioration were evaluated as prognostic indicators of treatment outcome. Generalized linear mixed model and regression tests were used to examine the prognostic value of CXR results. Among a total of 228 patients (mean 59.9 \u00b1 18.8 years old), consolidation scores had a significant association with erythrocyte sedimentation rate and C-reactive protein changes, and initial consolidation scores were associated with the last SpO2 result (estimate \u22120.018, p = 0.024). All consolidation scores during admission showed significant association with the total O2 supply period and the last SpO2 result. Early changing degree of consolidation score showed an association with deterioration (odds ratio 1.017, 95% confidence interval 1.005\u20131.03). In conclusion, AI-based CXR results for consolidation have potential prognostic value for predicting treatment outcomes in COVID-19 patients.<\/jats:p>","DOI":"10.3390\/diagnostics13122090","type":"journal-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T08:56:01Z","timestamp":1686905761000},"page":"2090","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Clinical Implication and Prognostic Value of Artificial-Intelligence-Based Results of Chest Radiographs for Assessing Clinical Outcomes of COVID-19 Patients"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7462-2609","authenticated-orcid":false,"given":"Hyun Joo","family":"Shin","sequence":"first","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\/0000-0003-0250-212X","authenticated-orcid":false,"given":"Min Hyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6192-8852","authenticated-orcid":false,"given":"Nak-Hoon","family":"Son","sequence":"additional","affiliation":[{"name":"Department of Statistics, Keimyung University, Daegu 42601, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyunghwa","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5081-7906","authenticated-orcid":false,"given":"Yong Chan","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, Department of Internal Medicine, 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":"Division of Infectious Diseases, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2570-3442","authenticated-orcid":false,"given":"Eun Hye","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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":[[2023,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1001\/jama.2020.12839","article-title":"Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): A review","volume":"324","author":"Wiersinga","year":"2020","journal-title":"Jama"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sharma, A., Ahmad Farouk, I., and Lal, S.K. 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