{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T13:02:26Z","timestamp":1792242146003,"version":"4.4.0"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003286","name":"Kuwait Foundation for the Advancement of Sciences","doi-asserted-by":"publisher","award":["CN23-13NR-2059"],"award-info":[{"award-number":["CN23-13NR-2059"]}],"id":[{"id":"10.13039\/501100003286","id-type":"DOI","asserted-by":"publisher"}]},{"award":["CN23-13NR-2059"],"award-info":[{"award-number":["CN23-13NR-2059"]}],"id":[{"id":"https:\/\/ror.org\/03xr9q522","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objective: Chest radiography (CXR) is routinely used in the evaluation of respiratory disease; however, differentiating COVID-19 from other viral pneumonias on CXR remains challenging due to substantial radiographic overlap. In this study, a deep learning-based CXR classification model using a ResNet-50 architecture was developed to categorize images as normal, COVID-19, or non-COVID viral pneumonia, with emphasis on bias mitigation and external validation. Methods: Model training and internal validation were performed using harmonized publicly available datasets with patient-level stratified five-fold cross-validation, while generalizability was evaluated using an independent real-world institutional dataset from Adan Hospital, Kuwait, which was excluded from all training, validation, and hyperparameter tuning stages. Results: On the public validation dataset (n = 847), the model achieved an overall accuracy of 96.8% with balanced class-wise performance, whereas performance on the independent institutional dataset (n = 320) decreased to 93.7%, consistent with expected domain shift. Calibration analyses demonstrated well-aligned probabilistic estimates on validation data and acceptable calibration on institutional data. Negative predictive values remained high for normal and COVID-19 classes across datasets. Exploratory decision curve analysis demonstrated net benefit patterns for COVID-19 predictions under hypothetical threshold assumptions. Conclusions: These findings indicate that, when developed with explicit bias-mitigation strategies and evaluated using independent institutional data, deep learning-based CXR analysis may provide supportive, non-diagnostic decision signals for radiology triage workflows; however, prospective multicenter validation is required prior to clinical adoption.<\/jats:p>","DOI":"10.3390\/diagnostics16070995","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:45:42Z","timestamp":1774626342000},"page":"995","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Externally Validated Deep Learning Analysis of Chest Radiographs for Differentiating COVID-19 and Viral Pneumonia"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0542-5924","authenticated-orcid":false,"given":"Michael","family":"Masoomi","sequence":"first","affiliation":[{"name":"Department of Radiology, Adan Hospital, Al-Mosely Street, Hadiya 46969, Kuwait"},{"name":"Department of Nuclear Medicine and Molecular Imaging, Adan Hospital, Hadiya 46969, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Latifa","family":"Al-Kandari","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine and Molecular Imaging, Adan Hospital, Hadiya 46969, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haytam","family":"Ramzy","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine and Molecular Imaging, Adan Hospital, Hadiya 46969, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahday Abass","family":"Hamza","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine and Molecular Imaging, Adan Hospital, Hadiya 46969, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1007\/s42979-021-00695-5","article-title":"Chest X-ray classification using deep learning for automated COVID-19 screening","volume":"2","author":"Shelke","year":"2021","journal-title":"SN Comput. 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