ControlEchoSynth: Boosting Ejection Fraction Estimation Models via Controlled Video Diffusion

TL;DR

This paper introduces a controlled video diffusion method to generate synthetic echocardiogram views, improving ejection fraction estimation accuracy and enhancing machine learning model robustness in cardiac ultrasound diagnostics.

Contribution

It presents a novel conditional generative model for synthetic echo view creation, specifically aimed at boosting ejection fraction estimation in echocardiography.

Findings

Synthetic echoes improve EF estimation accuracy.

Augmented datasets lead to more robust ML models.

Potential to advance clinical diagnostic tools.

Abstract

Synthetic data generation represents a significant advancement in boosting the performance of machine learning (ML) models, particularly in fields where data acquisition is challenging, such as echocardiography. The acquisition and labeling of echocardiograms (echo) for heart assessment, crucial in point-of-care ultrasound (POCUS) settings, often encounter limitations due to the restricted number of echo views available, typically captured by operators with varying levels of experience. This study proposes a novel approach for enhancing clinical diagnosis accuracy by synthetically generating echo views. These views are conditioned on existing, real views of the heart, focusing specifically on the estimation of ejection fraction (EF), a critical parameter traditionally measured from biplane apical views. By integrating a conditional generative model, we demonstrate an improvement in EF estimation accuracy, providing a comparative analysis with traditional methods. Preliminary results indicate that our synthetic echoes, when used to augment existing datasets, not only enhance EF estimation but also show potential in advancing the development of more robust, accurate, and clinically relevant ML models. This approach is anticipated to catalyze further research in synthetic data applications, paving the way for innovative solutions in medical imaging diagnostics.