Meng, Fanyang, Kottlors, Jonathan, Shahzad, Rahil, Liu, Haifeng, Fervers, Philipp, Jin, Yinhua, Rinneburger, Miriam, Le, Dou, Weisthoff, Mathilda, Liu, Wenyun, Ni, Mengzhe, Sun, Ye, An, Liying, Huai, Xiaochen, More, Dorottya, Giannakis, Athanasios, Kaltenborn, Isabel, Bucher, Andreas, Maintz, David, Zhang, Lei, Thiele, Frank, Li, Mingyang, Perkuhn, Michael, Zhang, Huimao and Persigehl, Thorsten . AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study. Eur. Radiol.. NEW YORK: SPRINGER. ISSN 1432-1084

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Abstract

Objectives Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. Methods We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence.Results The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores.Conclusion This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Meng, FanyangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kottlors, JonathanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Shahzad, RahilUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Liu, HaifengUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Fervers, PhilippUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Jin, YinhuaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Rinneburger, MiriamUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Le, DouUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Weisthoff, MathildaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Liu, WenyunUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Ni, MengzheUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Sun, YeUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
An, LiyingUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Huai, XiaochenUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
More, DorottyaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Giannakis, AthanasiosUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kaltenborn, IsabelUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bucher, AndreasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Maintz, DavidUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Zhang, LeiUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Thiele, FrankUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Li, MingyangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Perkuhn, MichaelUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Zhang, HuimaoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Persigehl, ThorstenUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-688617
DOI: 10.1007/s00330-022-09335-9
Journal or Publication Title: Eur. Radiol.
Publisher: SPRINGER
Place of Publication: NEW YORK
ISSN: 1432-1084
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
DISEASEMultiple languages
Radiology, Nuclear Medicine & Medical ImagingMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/68861

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