id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-034686-y0y5ltxs	Gieraerts, Christopher	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	2020-10-22		.txt	text/plain	3164	173	49	title: 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 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. 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). 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. For quantitative percentage of lung involvement, visual analysis demonstrated excellent agreement with AI-assisted analysis without and with manual correction (ICC: 0.873 and 0.871, respectively). On the basis of the interreader variability of chest CT, we estimated sample sizes needed to detect significant decreases in lung involvement during a clinical trial ( Figure 6 ).	./cache/cord-034686-y0y5ltxs.txt	./txt/cord-034686-y0y5ltxs.txt