{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T21:58:37Z","timestamp":1789682317979,"version":"4.0.1"},"reference-count":44,"publisher":"Georg Thieme Verlag KG","issue":"02","license":[{"start":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T00:00:00Z","timestamp":1745884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sino-German Center for Research Promotion (SGC), a project entitled CT-based Deep Learning Algorithm in Diagnosis and evaluation of COVID-19: An International Multi-center Study","award":["C-0007"],"award-info":[{"award-number":["C-0007"]}]},{"name":"Jilin Provincial Key Laboratory of Medical imaging & big data","award":["20200601003JC"],"award-info":[{"award-number":["20200601003JC"]}]},{"name":"Radiology and Technology Innovation Center of Jilin Province","award":["20190902016TC"],"award-info":[{"award-number":["20190902016TC"]}]},{"name":"China International Medical Foundation, Imaging Research, SKY","award":["Z-2014-07-2003-03"],"award-info":[{"award-number":["Z-2014-07-2003-03"]}]},{"name":"RACOON (NUM), \u201eNUM 2.0\u201c","award":["FKZ: 01KX2121"],"award-info":[{"award-number":["FKZ: 01KX2121"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Rofo"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>AI is emerging as a promising tool for diagnosing COVID-19 based on chest CT scans. The aim of this study was the comparison of AI models for COVID-19 diagnosis. Therefore, we: (1) trained three distinct AI models for classifying COVID-19 and non-COVID-19 pneumonia (nCP) using a large, clinically relevant CT dataset, (2) evaluated the models\u2019 performance using an independent test set, and (3) compared the models both algorithmically and experimentally.<\/jats:p>\n                  <jats:p>In this multicenter multi-vendor study, we collected n=1591 chest CT scans of COVID-19 (n=762) and nCP (n=829) patients from China and Germany. In Germany, the data was collected from three RACOON sites. We trained and validated three COVID-19 AI models with different architectures: COVNet based on 2D-CNN, DeCoVnet based on 3D-CNN, and AD3D-MIL based on 3D-CNN with attention module. 991 CT scans were used for training the AI models using 5-fold cross-validation. 600 CT scans from 6 different centers were used for independent testing. The models\u2019 performance was evaluated using accuracy (Acc), sensitivity (Se), and specificity (Sp).<\/jats:p>\n                  <jats:p>The average validation accuracy of the COVNet, DeCoVnet, and AD3D-MIL models over the 5 folds was 80.9%, 82.0%, and 84.3%, respectively. On the independent test set with n=600 CT scans, COVNet yielded Acc=76.6%, Se=67.8%, Sp=85.7%; DeCoVnet provided Acc=75.1%, Se=61.2%, Sp=89.7%; and AD3D-MIL achieved Acc=73.9%, Se=57.7%, Sp=90.8%.<\/jats:p>\n                  <jats:p>The classification performance of the evaluated AI models is highly dependent on the training data rather than the architecture itself. Our results demonstrate a high specificity and moderate sensitivity. The AI classification models should not be used unsupervised but could potentially assist radiologists in COVID-19 and nCP identification.<\/jats:p>","DOI":"10.1055\/a-2577-3928","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T19:07:58Z","timestamp":1745953678000},"page":"185-198","source":"Crossref","is-referenced-by-count":2,"title":["Performance of AI Approaches for COVID-19 Diagnosis Using Chest CT Scans: The Impact of Architecture and Dataset"],"prefix":"10.1055","volume":"198","author":[{"given":"Astha","family":"Jaiswal","sequence":"additional","affiliation":[{"name":"Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp","family":"Fervers","sequence":"additional","affiliation":[{"name":"Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanyang","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Hospital of Jilin University, Changchun, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huimao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Hospital of Jilin University, Changchun, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dorottya","family":"M\u00f3r\u00e9","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, University of Heidelberg, Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Athanasios","family":"Giannakis","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, University of Heidelberg, Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasmin","family":"Wailzer","sequence":"additional","affiliation":[{"name":"Institute for Diagnostic and Interventional Radiology, Frankfurt University Hospital, Frankfurt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas Michael","family":"Bucher","sequence":"additional","affiliation":[{"name":"Institute for Diagnostic and Interventional Radiology, Frankfurt University Hospital, Frankfurt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Maintz","sequence":"additional","affiliation":[{"name":"Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5021-6895","authenticated-orcid":false,"given":"Jonathan","family":"Kottlors","sequence":"additional","affiliation":[{"name":"Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahil","family":"Shahzad","sequence":"additional","affiliation":[{"name":"Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany"},{"name":"Philips Healthcare, Innovative Technologies, Aachen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5928-4405","authenticated-orcid":false,"given":"Thorsten","family":"Persigehl","sequence":"additional","affiliation":[{"name":"Institute for Diagnostic and 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