Article ; Online: 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.
Radiology. Cardiothoracic imaging
2020 Volume 2, Issue 5, Page(s) e200441
Abstract: 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 ...
Abstract | 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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Language | English |
Publishing date | 2020-10-22 |
Publishing country | United States |
Document type | Journal Article |
ISSN | 2638-6135 |
ISSN (online) | 2638-6135 |
DOI | 10.1148/ryct.2020200441 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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