Online Archive of University of Virginia Scholarship
Development of Deep Learning Models for Predicting Cardiac Related Outcomes; Gender Bias in AI-Powered Cardiac MRI Diagnostics: Examining Disparities in Medical Outcomes139 views
Author
Le, Jonathan, School of Engineering and Applied Science, University of Virginia
Advisors
Earle, Joshua, University of Virginia
Zhang, Miaomiao, EN-Elec & Comp Engr Dept, University of Virginia
Bilchick, Kenneth, University of Virginia
Abstract
Artificial intelligence (AI) has had a significant impact on cardiac medicine, enhancing
diagnosis, treatment planning, and patient care across multiple clinical settings. Such major
advances include increased ability for interpreting data from MRIs and ECGs to predict clinical
outcomes such as. In this portfolio, I present two complementary reports that explore both the
societal implications of AI in cardiovascular care and a technical analysis of my deep learning
contributions to this rapidly evolving field.
Degree
BS (Bachelor of Science)
Keywords
Cardiology; ECG; Deep Learning; bioinformatics; Convolutional Neural Networks; Venctricular Arrythmia
Notes
School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Miaomiao Zhang, Kenneth Bilchick, Derek Bivona
STS Advisor: Joshua Earle
Technical Team Members: Miaomiao Zhang, Kenneth Bilchick, Derek Bivona
Language
English
Rights
All rights reserved (no additional license for public reuse)
Le, Jonathan. Development of Deep Learning Models for Predicting Cardiac Related Outcomes; Gender Bias in AI-Powered Cardiac MRI Diagnostics: Examining Disparities in Medical Outcomes. University of Virginia, School of Engineering and Applied Science, BS (Bachelor of Science), 2025-05-08, https://doi.org/10.18130/p5my-jz68.
