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Kondori, Nima

University of British Columbia

Ejection fraction (EF) serves as a critical indicator of cardiac function, traditionally assessed through expert clinicians' manual interpretation of echocardiograms. However, the labor-intensive nature of this process, along with inter-observer variability and data scarcity, highlights the need for automated and scalable solutions. This thesis explores the application of video diffusion models to generate synthetic echocardiograms as a means to augment limited datasets, thereby enhancing ejection fraction estimation models. By leveraging synthetic data generation, we aim to address data scarcity, enhance model performance, and validate the effectiveness of synthetic data in echocardiography (echo). The proposed methodology integrates diffusion models with echo video data to create realistic cardiac echocardiograms tailored to each patient. We also develop a data augmentation framework aiming at improving EF estimation. Extensive experiments are conducted to evaluate the contribution of the synthetic data to model performance on EF prediction accuracy, focusing on scenarios with limited labeled data. Our results demonstrate that incorporating diffusion-augmented training data leads to improvements in both the accuracy and robustness of automated EF estimation models. In addition, we investigate and present various strategies for the rapid generation of synthetic echocardiograms through model distillation. Our preliminary findings establish a foundation for future research in real-time echocardiogram synthesis, facilitating applications in clinical training and procedural guidance. Ultimately, this thesis provides a novel approach to controlled synthetic data-driven augmentation, contributing to the broader field of cardiac imaging by enabling more efficient and precise diagnostic tools. This work advances the potential for scalable, Artificial Intelligence (AI)-driven cardiac assessments, offering enhanced accessibility to high-quality care in high-resource and low-resource clinical environments.

Text

2025-04-22

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/90702

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/