Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography

Heart disease is the global leading cause of death. A key predictor of heart failure and the most commonly measured cardiac parameter is left ventricular ejection fraction (LVEF). Despite available segmentation technologies, experienced cardiologists often rely on visual estimation of LVEF for a swift assessment. In this paper, we present a direct dual-channel LVEF estimation approach that mimics cardiologists' visual assessment for detecting patients with high risk of systolic heart failure. The proposed framework consists of various layers for extracting spatial and temporal features from echocardiography (echo) cines. A data set of 1,186 apical two-chamber (A2C) and four-chamber (A4C) echo cines were used in this study. LVEF labels were assigned based on risk of heart failure: high-risk for LVEF <= 40% and low-risk for 40% < LVEF <= 75%. We validated the proposed framework on 237 clinical exams and achieved a success rate of 83.1% for risk-based LVEF classification. Our experiments suggests the fusion of the two apical views improves the performance, compared to single-view networks, especially A2C. The proposed solution is promising for segmentation-free detection of high-risk LVEF. Direct LVEF estimation eliminates ventricle segmentation, and can hence be a useful tool for formal echo and point-of-care cardiac ultrasound.