  The echocardiographic measurement of left ventricular ejection fraction
(LVEF) is fundamental to the diagnosis and classification of patients with
heart failure (HF). In order to quantify LVEF automatically and accurately,
this paper proposes a new pipeline method based on deep neural networks and
ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network
(ACNN) was first trained to segment the left ventricle (LV), before employing
the area-length formulation based on the ellipsoid single-plane model to
calculate LVEF values. This formulation required inputs of LV area, derived
from segmentation using an improved Jeffrey's method, as well as LV length,
derived from a novel ensemble learning model. To further improve the pipeline's
accuracy, an automated peak detection algorithm was used to identify
end-diastolic and end-systolic frames, avoiding issues with human error.
Subsequently, single-beat LVEF values were averaged across all cardiac cycles
to obtain the final LVEF. This method was developed and internally validated in
an open-source dataset containing 10,030 echocardiograms. The Pearson's
correlation coefficient was 0.83 for LVEF prediction compared to expert human
analysis (p<0.001), with a subsequent area under the receiver operator curve
(AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF
with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200
echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval
0.88 to 0.91) for HFrEF assessment. This study demonstrates that an automated
neural network-based calculation of LVEF is comparable to expert clinicians
performing time-consuming, frame-by-frame manual evaluation of cardiac systolic
function.
