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

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 measurement of LV volumes and function. However, boundary identification is still prone to errors limiting accuracy in certain patients. We hypothesized that a fully automated machine learning algorithm could circumvent border detection and instead would estimate the degree of ventricular contraction, similar to a human expert trained on tens of thousands of images.

Methods: Machine learning algorithm was developed and trained to automatically estimate LVEF on a database of >50 000 echocardiographic studies, including multiple apical 2- and 4-chamber views (AutoEF, BayLabs). Testing was performed on an independent group of 99 patients, whose automated EF values were compared with reference values obtained by averaging measurements by 3 experts using conventional volume-based technique. Inter-technique agreement was assessed using linear regression and Bland-Altman analysis. Consistency was assessed by mean absolute deviation among automated estimates from different combinations of apical views. Finally, sensitivity and specificity of detecting of EF ≤35% were calculated. These metrics were compared side-by-side against the same reference standard to those obtained from conventional EF measurements by clinical readers.

Results: Automated estimation of LVEF was feasible in all 99 patients. AutoEF values showed high consistency (mean absolute deviation =2.9%) and excellent agreement with the reference values: r=0.95, bias=1.0%, limits of agreement =±11.8%, with sensitivity 0.90 and specificity 0.92 for detection of EF ≤35%. This was similar to clinicians' measurements: r=0.94, bias=1.4%, limits of agreement =±13.4%, sensitivity 0.93, specificity 0.87.

Conclusions: Machine learning algorithm for volume-independent LVEF estimation is highly feasible and similar in accuracy to conventional volume-based measurements, when compared with reference values provided by an expert panel.

Keywords: echocardiography; endocardium; left ventricular function; machine learning; observer variation.

Figure 1.

Schematic representation of the left ventricle with its longitudinal and radial dimensions L and R, which change over time from its initial values at time 0, namely L(0) and R(0) according to dimensionless time-dependent contraction coefficients CL(t) and CR(t), which can be used to calculate ejection fraction (see text for details).

Figure 2.

Agreement between the machine learning-based automated ejection fraction (EF) measurements (left), side-by-side with the clinical measurements (right) against reference values obtained by averaging measurements by a panel of 3 experts: linear regression (top) and Bland-Altman analysis (bottom).

Figure 3.

Agreement between the machine learning-based automated ejection fraction (EF) measurements without (left) and with de-trending correction (right) against reference values obtained by averaging measurements by a panel of 3 experts: linear regression (top) and Bland-Altman analysis (bottom).