  Non-invasive and cost effective in nature, the echocardiogram allows for a
comprehensive assessment of the cardiac musculature and valves. Despite
progressive improvements over the decades, the rich temporally resolved data in
echocardiography videos remain underutilized. Human reads of echocardiograms
reduce the complex patterns of cardiac wall motion, to a small list of
measurements of heart function. Furthermore, all modern echocardiography
artificial intelligence (AI) systems are similarly limited by design -
automating measurements of the same reductionist metrics rather than utilizing
the wealth of data embedded within each echo study. This underutilization is
most evident in situations where clinical decision making is guided by
subjective assessments of disease acuity, and tools that predict disease onset
within clinically actionable timeframes are unavailable. Predicting the
likelihood of developing post-operative right ventricular failure (RV failure)
in the setting of mechanical circulatory support is one such clinical example.
To address this, we developed a novel video AI system trained to predict
post-operative right ventricular failure (RV failure), using the full
spatiotemporal density of information from pre-operative echocardiography
scans. We achieve an AUC of 0.729, specificity of 52% at 80% sensitivity and
46% sensitivity at 80% specificity. Furthermore, we show that our ML system
significantly outperforms a team of human experts tasked with predicting RV
failure on independent clinical evaluation. Finally, the methods we describe
are generalizable to any cardiac clinical decision support application where
treatment or patient selection is guided by qualitative echocardiography
assessments.
