2021/03/03 by Rohan Shad, Shad, Rohan, Nicolas Quach +20
Computer Science · Medicine · #Cardiac Imaging and Diagnostics #Cardiovascular Function and Risk Factors #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning in Healthcare #cs.CV
paper · pdf · doi:10.48550/arxiv.2103.02583
9 pages, 5 figures
arxiv created 2021/03/03 · openalex publication_date 2021/03/03 · arxiv updated 2021/03/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.