Using artificial intelligence to improve the diagnostic efficiency of pulmonologists in differentiating COVID-19 pneumonia from community-acquired pneumonia

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dc.authoridKavuran, Gurkan/0000-0003-2651-5005
dc.authoridIN, ERDAL/0000-0002-8807-5853
dc.authorwosidŞAHİN, MAHMUT/HKW-3047-2023
dc.authorwosidKuluöztürk, Mutlu/X-8228-2018
dc.authorwosidKavuran, Gurkan/S-6935-2016
dc.contributor.authorIn, Erdal
dc.contributor.authorGeckil, Aysegul A.
dc.contributor.authorKavuran, Gurkan
dc.contributor.authorSahin, Mahmut
dc.contributor.authorBerber, Nurcan K.
dc.contributor.authorKuluozturk, Mutlu
dc.date.accessioned2024-08-04T21:02:23Z
dc.date.available2024-08-04T21:02:23Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractCoronavirus disease 2019 (COVID-19) has quickly turned into a global health problem. Computed tomography (CT) findings of COVID-19 pneumonia and community-acquired 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-19 pneumonia from CAP using CT scans. A deep learning-based AI model was created to be utilized in the detection of COVID-19, 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-19 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-19 pneumonia both with and without the help of the AI. The accuracy, sensitivity, and specificity values of the proposed AI model for determining COVID-19 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%, p < 0.001), sensitivity (79.1% vs. 70%, p < 0.001), and specificity (96.5% vs. 87.5%, p < 0.001). AI support significantly increases the diagnostic efficiency of pulmonologists in the diagnosis of COVID-19 via CT. Studies in the future should focus on real-time applications of AI to fight the COVID-19 infection.en_US
dc.identifier.doi10.1002/jmv.27777
dc.identifier.endpage3705en_US
dc.identifier.issn0146-6615
dc.identifier.issn1096-9071
dc.identifier.issue8en_US
dc.identifier.pmid35419818en_US
dc.identifier.startpage3698en_US
dc.identifier.urihttps://doi.org/10.1002/jmv.27777
dc.identifier.urihttps://hdl.handle.net/11616/104697
dc.identifier.volume94en_US
dc.identifier.wosWOS:000789477300001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofJournal of Medical Virologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectartificial intelligenceen_US
dc.subjectcommunity-acquired pneumoniaen_US
dc.subjectcomputed tomographyen_US
dc.subjectcoronavirus disease 2019en_US
dc.subjectdeep learningen_US
dc.titleUsing artificial intelligence to improve the diagnostic efficiency of pulmonologists in differentiating COVID-19 pneumonia from community-acquired pneumoniaen_US
dc.typeArticleen_US

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