Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert.

Federico M Asch¹, Nicolas Poilvert², Theodore Abraham³, Madeline Jankowski⁴, Jayne Cleve⁵, Michael Adams², Nathanael Romano², Ha Hong², Victor Mor-Avi⁶, Randolph P Martin^2,7, Roberto M Lang⁶

¹MedStar Health Research Institute, Washington DC (F.M.A.).

²Bay Labs Inc, San Francisco, CA (N.P., M.A., N.R., H.H., R.P.M.).

³University of California, San Francisco, CA (T.A.).

⁴Northwestern Memorial Hospital, Chicago, IL (M.J.).

⁵Duke University Medical Center, Chapel Hill, NC (J.C.).

⁶University of Chicago Medical Center, Chicago, IL (V.M.-A., R.M.L.).

⁷Emory University Medical Center, Atlanta, GA (R.P.M.).

PubMed PMC DOI PDF

Abstract

BACKGROUND: Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by model-based calculation of end-systolic and end-diastolic LV volumes. Recent developments in artificial intelligence resulted in computer algorithms that allow near automated detection of endocardial boundaries and me…

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