Fully Automated Versus Standard Tracking of Left Ventricular Ejection Fraction and Longitudinal Strain: The FAST-EFs...

Echocardiographic determination of ejection fraction (EF) by manual tracing of endocardial borders is time consuming and operator dependent, whereas visual assessment is inherently subjective.

This study tested the hypothesis that a novel, fully automated software using machine learning-enabled image analysis will provide rapid, reproducible measurements of left ventricular volumes and EF, as well as average biplane longitudinal strain (LS).

For a total of 255 patients in sinus rhythm, apical 4- and 2-chamber views were collected from 4 centers that assessed EF using both visual estimation and manual tracing (biplane Simpson's method). In addition, datasets were saved in a centralized database, and machine learning-enabled software (AutoLV, TomTec-Arena 1.2, TomTec Imaging Systems, Unterschleissheim, Germany) was applied for fully automated EF and LS measurements. A reference center reanalyzed all datasets (by visual estimation and manual tracking), along with manual LS determinations.

AutoLV measurements were feasible in 98% of studies, and the average analysis time was 8 ± 1 s/patient. Interclass correlation coefficients and Bland-Altman analysis revealed good agreements among automated EF, local center manual tracking, and reference center manual tracking, but not for visual EF assessments. Similarly, automated and manual LS measurements obtained at the reference center showed good agreement. Intraobserver variability was higher for visual EF than for manual EF or manual LS, whereas interobserver variability was higher for both visual and manual EF, but not different for LS. Automated EF and LS had no variability.

Fully automated analysis of echocardiography images provides rapid and reproducible assessment of left ventricular EF and LS.