{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T03:26:04Z","timestamp":1790997964403,"version":"4.1.0"},"reference-count":25,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T00:00:00Z","timestamp":1651449600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T00:00:00Z","timestamp":1651449600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Medical Virology"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Coronavirus disease 2019 (COVID\u201019) has quickly turned into a global health problem. Computed tomography (CT) findings of COVID\u201019 pneumonia and community\u2010acquired pneumonia (CAP) may be similar. Artificial intelligence (AI) is a popular topic among medical imaging techniques and has caused significant developments in diagnostic techniques. This retrospective study aims to analyze the contribution of AI to the diagnostic performance of pulmonologists in distinguishing COVID\u201019 pneumonia from CAP using CT scans. A deep learning\u2010based AI model was created to be utilized in the detection of COVID\u201019, which extracted visual data from volumetric CT scans. The final data set covered a total of 2496 scans (887 patients), which included 1428 (57.2%) from the COVID\u201019 group and 1068 (42.8%) from the CAP group. CT slices were classified into training, validation, and test datasets in an 8:1:1. The independent test data set was analyzed by comparing the performance of four pulmonologists in differentiating COVID\u201019 pneumonia both with and without the help of the AI. The accuracy, sensitivity, and specificity values of the proposed AI model for determining COVID\u201019 in the independent test data set were 93.2%, 85.8%, and 99.3%, respectively, with the area under the receiver operating characteristic curve of 0.984. With the assistance of the AI, the pulmonologists accomplished a higher mean accuracy (88.9% vs. 79.9%,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001), sensitivity (79.1% vs. 70%,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001), and specificity (96.5% vs. 87.5%,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). AI support significantly increases the diagnostic efficiency of pulmonologists in the diagnosis of COVID\u201019 via CT. Studies in the future should focus on real\u2010time applications of AI to fight the COVID\u201019 infection.\n                  <\/jats:p>","DOI":"10.1002\/jmv.27777","type":"journal-article","created":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T23:10:29Z","timestamp":1649891429000},"page":"3698-3705","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Using artificial intelligence to improve the diagnostic efficiency of pulmonologists in differentiating COVID\u201019 pneumonia from community\u2010acquired pneumonia"],"prefix":"10.1002","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8807-5853","authenticated-orcid":false,"given":"Erdal","family":"\u0130n","sequence":"first","affiliation":[{"name":"Department of Pulmonary Medicine, School of Medicine Malatya Turgut Ozal University Malatya Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0348-3194","authenticated-orcid":false,"given":"Ay\u015feg\u00fcl A.","family":"Ge\u00e7kil","sequence":"additional","affiliation":[{"name":"Department of Pulmonary Medicine, School of Medicine Malatya Turgut Ozal University Malatya Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00fcrkan","family":"Kavuran","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences Malatya Turgut Ozal University Malatya Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmut","family":"\u015eahin","sequence":"additional","affiliation":[{"name":"Department of Radiology Malatya Training and Research Hospital Malatya Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8634-2543","authenticated-orcid":false,"given":"Nurcan K.","family":"Berber","sequence":"additional","affiliation":[{"name":"Department of Pulmonary Medicine, School of Medicine Malatya Turgut Ozal University Malatya Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2749-9166","authenticated-orcid":false,"given":"Mutlu","family":"Kulu\u00f6zt\u00fcrk","sequence":"additional","affiliation":[{"name":"Department of Pulmonary Medicine, School of Medicine Firat University Elaz\u0131\u011f Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,5,2]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41564-020-0695-z"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmv.25722"},{"key":"e_1_2_9_4_1","unstructured":"Johns Hopkins Coronavirus Resource Center. 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