  Integrating methods for time-to-event prediction with diagnostic imaging
modalities is of considerable interest, as accurate estimates of survival
requires accounting for censoring of individuals within the observation period.
New methods for time-to-event prediction have been developed by extending the
cox-proportional hazards model with neural networks. In this paper, to explore
the feasibility of these methods when applied to deep learning with
echocardiography videos, we utilize the Stanford EchoNet-Dynamic dataset with
over 10,000 echocardiograms, and generate simulated survival datasets based on
the expert annotated ejection fraction readings. By training on just the
simulated survival outcomes, we show that spatiotemporal convolutional neural
networks yield accurate survival estimates.
