Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography

BACKGROUND : Transthoracic echocardiography (TTE) is the leading cardiac imaging modality for patients admitted with COVID-19 infection, a condition of high short-term mortality. We aimed to test the hypothesis that artificial intelligence (AI) based analysis of echocardiographic images could predict mortality more accurately than conventional analysis by a human expert.

METHODS : Patients admitted to 13 hospitals for acute COVID-19 disease who had a TTE were included. Left ventricular (LV) ejection fraction (EF) and LV longitudinal strain (LS) were obtained manually by multiple expert readers and by an automated, AI software. The ability of the manual and AI analyses to predict all-cause mortality was compared.

RESULTS : 870 patients were enrolled, mortality was 27.4% at a follow-up of 230±115 days. AI analysis had lower variability than manual for both LV EF (p=0.003) and LS (p=0.005). AI-derived LV EF and LS were predictors of mortality in univariable and multivariable regression analysis (OR=0.974, 95% CI= 0.956-0.991, p=0.003 for EF; OR=1.060, 95% CI 1.019-1.105, p=0.004 for LS), but LV EF and LS obtained by manual analysis were not. Direct comparison of predictive value of AI vs manual measurements of LV EF and LS was significantly better for AI (p=0.005 and 0.003 respectively). In addition, AI-derived LV EF and LS had more significant and stronger correlations to other objective biomarkers for acute disease than manual reads.

CONCLUSIONS : AI-based analysis of LVEF and LVLS had a similar feasibility to manual analysis, minimized variability and consequently increased the statistical power to predict mortality. AI-based analyses, but not manual, were significant predictors of in-hospital and follow-up mortality.

Asch Federico M, Descamps Tine, Sarwar Rizwan, Karagodin Ilya, Singulane Cristiane Carvalho, Xie Mingxing, Tucay Edwin S, Tude Rodrigues Ana C, Vasquez-Ortiz Zuilma Y, Monaghan Mark J, Ordonez Salazar Bayardo A, Soulat-Dufour Laurie, Alizadehasl Azin, Mostafavi Atoosa, Moreo Antonella, Citro Rodolfo, Narang Akhil, Wu Chun, Addetia Karima, Upton Ross, Woodward Gary M, Lang Roberto M


Artificial Intelligence, COVID-19, Echocardiography, Left Ventricular Function, Machine Learning, Outcomes Prediction, WASE