Measuring the Left Ventricular Ejection Fraction using Geometric Features

One of the crucial indicators of the heart's functioning, is the so-called left ventricular ejection fraction (LVEF), which measures the heart's ability to pump blood, and corresponds to the relative change in volume within the heart's left ventricle between it's most expanded (end-diastole) and most contracted state (end-systole) during a cardiac cycle. A reduced LVEF is a key indicator of heart failure, and as such, its accurate measurement plays a prominent role in cardiology. This work proposes a machine learning approach for estimating the LVEF from short echocardiogram videos. Our model, based on gradient-boosted trees, is significantly simpler than the state of the art, but is competitive in terms of accuracy and has a higher degree of explainability. The proposed model operates on a set of geometric features of the heart's left ventricle, tracking its evolution during the cardiac cycle; some of these features are novel and are proposed here for the first time. We discuss the performance of our model on a dataset of over 10,000 samples, including the relative importance of our proposed features, and show that the model's estimation error is well within the margin of variation that occurs when the same LVEF is measured by different experts.