Jungmann, Florian, Mueller, Lukas, Hahn, Felix, Weustenfeld, Maximilian, Dapper, Ann-Kathrin, Maehringer-Kunz, Aline, Graafen, Dirk, Dueber, Christoph, Schafigh, Darius, Pinto dos Santos, Daniel, Mildenberger, Peter and Kloeckner, Roman . Commercial Al solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation? Eur. Radiol.. NEW YORK: SPRINGER. ISSN 1432-1084

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Abstract

Objectives In response to the COVID-19 pandemic, many researchers have developed artificial intelligence (AI) tools to differentiate COVID-19 pneumonia from other conditions in chest CT. However, in many cases, performance has not been clinically validated. The aim of this study was to evaluate the performance of commercial AI solutions in differentiating COVID-19 pneumonia from other lung conditions. Methods Four commercial AI solutions were evaluated on a dual-center clinical dataset consisting of 500 CT studies; COVID-19 pneumonia was microbiologically proven in 50 of these. Sensitivity, specificity, positive and negative predictive values, and AUC were calculated. In a subgroup analysis, the performance of the AI solutions in differentiating COVID-19 pneumonia from other conditions was evaluated in CT studies with ground-glass opacities (GGOs). Results Sensitivity and specificity ranges were 62-96% and 31-80%, respectively. Negative and positive predictive values ranged between 82-99% and 19-25%, respectively. AUC was in the range 0.54-0.79. In CT studies with GGO, sensitivity remained unchanged. However, specificity was lower, and ranged between 15 and 53%. AUC for studies with GGO was in the range 0.51 0.69. Conclusions This study highlights the variable specificity and low positive predictive value of AI solutions in diagnosing COVID-19 pneumonia in chest CT. However, one solution yielded acceptable values for sensitivity. Thus, with further improvement, commercial AI solutions currently under development have the potential to be integrated as alert tools in clinical routine workflow. Randomized trials are needed to assess the true benefits and also potential harms of the use of AI in image analysis.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Jungmann, FlorianUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Mueller, LukasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Hahn, FelixUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Weustenfeld, MaximilianUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Dapper, Ann-KathrinUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Maehringer-Kunz, AlineUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Graafen, DirkUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Dueber, ChristophUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Schafigh, DariusUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Pinto dos Santos, DanielUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Mildenberger, PeterUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kloeckner, RomanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-569808
DOI: 10.1007/s00330-021-08409-4
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
Radiology, Nuclear Medicine & Medical ImagingMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/56980

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