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Published on: December 19, 2020
Prognostic Value and Reproducibility of AI-assisted Analysis of Lung Involvement in COVID-19 on Low-Dose
Christopher Gieraerts1, Anthony Dangis1, Lode Janssen1
1Department of Radiology - Imelda Hospital, Bonheiden, Belgium (C.G., A.D., L.J., A.D., Y.D.B., R.S.); Department of Pulmonology - Imelda Hospital, Bonheiden, Belgium (N.D.B., A.V.D.B., T.L., A.H., E.F.); Department of Intensive Care Medicine - Imelda Hospital, Bonheiden, Belgium (E.F.); Department of Geriatrics - Imelda Hospital, Bonheiden, Belgium (M.L.); Department of Cardiology - Imelda Hospital, Bonheiden, Belgium (B.E., J.R.); Department of Medical Microbiology - Imelda Hospital, Bonheiden, Belgium (A.S., J.F.); Department of Emergency Medicine - Imelda Hospital, Bonheiden, Belgium (M.G.).
Purpose:
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.
Materials And Methods:
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.
Results:
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<0.001). Intrareader and interreader agreement rates were significantly higher for AI-assisted analysis than visual analysis (all ICC ≥0.960 versus ≥0.885). AI-assisted variability for quantitative percentage of lung involvement was 17.2% (coefficient of variation) versus 34.7% for visual analysis. The sample size to detect a 5% change in lung involvement with 90% power and an α error of 0.05 was 250 patients with AI-assisted analysis and 1014 patients with visual analysis.
Conclusion:
AI-assisted analysis of lung involvement on submillisievert low-dose chest CT outperformed conventional visual analysis in predicting outcome in COVID-19 patients while reducing CT variability. Lung involvement on chest CT could be used as a reliable metric in future clinical trials.
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