This Project Grant award of $174,740, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, will support research to improve the integration of deep learning models for electrocardiogram (ECG) diagnostic applications. The key objectives are to: 1) develop techniques for generating synthetic ECG signals to address data shortages for rare cardiac conditions, 2) enhance the reliability of deep learning models through uncertainty identification, 3) create clinically-aligned explanations for model decisions to increase trust and usability in real-world settings, and 4) facilitate the integration of interpretable and reliable deep learning systems into clinical workflows. This research, conducted in collaboration with clinical partners, aims to address predominant challenges in translating deep learning for ECG-based cardiac condition detection and monitoring. The project will run from August 1, 2025 to July 31, 2027 and is being led by the University of Texas at Tyler.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $174.7k | 6/28/25 |