{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T03:44:16Z","timestamp":1780544656778,"version":"3.54.1"},"reference-count":46,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T00:00:00Z","timestamp":1620345600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>\n                    The development of medical assisting tools based on artificial intelligence advances is essential in the global fight against COVID-19 outbreak and the future of medical systems. In this study, we introduce\n                    <jats:italic>ai-corona<\/jats:italic>\n                    , a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using chest CT scans. Our framework incorporates an EfficientNetB3-based feature extractor. We employed three datasets; the CC-CCII set, the MasihDaneshvari Hospital (MDH) cohort, and the MosMedData cohort. Overall, these datasets constitute 7184 scans from 5693 subjects and include the COVID-19, non-COVID abnormal (NCA), common pneumonia (CP), non-pneumonia, and Normal classes. We evaluate\n                    <jats:italic>ai-corona<\/jats:italic>\n                    on test sets from the CC-CCII set, MDH cohort, and the entirety of the MosMedData cohort, for which it gained AUC scores of 0.997, 0.989, and 0.954, respectively. Our results indicates\n                    <jats:italic>ai-corona<\/jats:italic>\n                    outperforms all the alternative models. Lastly, our framework\u2019s diagnosis capabilities were evaluated as assistant to several experts. Accordingly, We observed an increase in both speed and accuracy of expert diagnosis when incorporating\n                    <jats:italic>ai-corona<\/jats:italic>\n                    \u2019s assistance.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0250952","type":"journal-article","created":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T14:27:06Z","timestamp":1620397626000},"page":"e0250952","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":47,"title":["ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans"],"prefix":"10.1371","volume":"16","author":[{"given":"Mehdi","family":"Yousefzadeh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parsa","family":"Esfahanian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed Mohammad Sadegh","family":"Movahed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5898-4872","authenticated-orcid":true,"given":"Saeid","family":"Gorgin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0104-4016","authenticated-orcid":true,"given":"Dara","family":"Rahmati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atefeh","family":"Abedini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed Alireza","family":"Nadji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Haseli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehrdad","family":"Bakhshayesh Karam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arda","family":"Kiani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meisam","family":"Hoseinyazdi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jafar","family":"Roshandel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1768-8342","authenticated-orcid":true,"given":"Reza","family":"Lashgari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,5,7]]},"reference":[{"key":"pone.0250952.ref001","unstructured":"\u201cCoronavirus Cases.\u201d Worldometer, www.worldometers.info\/coronavirus\/."},{"issue":"10223","key":"pone.0250952.ref002","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","article-title":"Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China","volume":"395","author":"C Huang","year":"2020","journal-title":"The lancet"},{"issue":"10223","key":"pone.0250952.ref003","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/S0140-6736(20)30211-7","article-title":"Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study","volume":"395","author":"N Chen","year":"2020","journal-title":"The lancet"},{"key":"pone.0250952.ref004","article-title":"First 12 patients with coronavirus disease 2019 (COVID-19) in the United States","author":"SA Kujawski","year":"2020","journal-title":"MedRxiv"},{"issue":"2","key":"pone.0250952.ref005","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1038\/s41418-020-00720-9","article-title":"COVID-19 vaccines: where we stand and challenges ahead","volume":"28","author":"G Forni","year":"2021","journal-title":"Cell Death Differentiation"},{"key":"pone.0250952.ref006","doi-asserted-by":"crossref","unstructured":"Centers for Disease Control and Prevention. 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