Prognostic Value and Reproducibility of AI-assisted Analysis of Lung Involvement in COVID-19 on Low-Dose Submillisievert Chest CT: Sample Size Implications for Clinical Trials

• Published in: Radiology. Cardiothoracic imaging 2020-10, Vol.2 (5), p.e200441-e200441
• Main Authors: Gieraerts, Christopher, Dangis, Anthony, Janssen, Lode, Demeyere, Annick, De Bruecker, Yves, De Brucker, Nele, van Den Bergh, Annelies, Lauwerier, Tine, Heremans, André, Frans, Eric, Laurent, Michaël, Ector, Bavo, Roosen, John, Smismans, Annick, Frans, Johan, Gillis, Marc, Symons, Rolf
• Format: Article
• Language: English
• Subjects: Original Research
• ISSN: 2638-6135; 2638-6135
• DOI: 10.1148/ryct.2020200441

To compare the prognostic value and reproducibility of visual versus AI-assisted analysis of lung involvement on submillisievert low-dose chest CT in COVID-19 patients. This was a HIPAA-compliant, institutional review board-approved retrospective study. From March 15 to June 1, 2020, 250 RT-PCR confirmed COVID-19 patients were studied with low-dose chest CT at admission. Visual and AI-assisted analysis of lung involvement was performed by using a semi-quantitative CT score and a quantitative percentage of lung involvement. Adverse outcome was defined as intensive care unit (ICU) admission or death. Cox regression analysis, Kaplan-Meier curves, and cross-validated receiver operating characteristic curve with area under the curve (AUROC) analysis was performed to compare model performance. Intraclass correlation coefficients (ICCs) and Bland- Altman analysis was used to assess intra- and interreader reproducibility. Adverse outcome occurred in 39 patients (11 deaths, 28 ICU admissions). AUC values from AI-assisted analysis were significantly higher than those from visual analysis for both semi-quantitative CT scores and percentages of lung involvement (all P