ControlEchoSynth: Boosting Ejection Fraction Estimation Models via Controlled Video Diffusion

2025-08-24

AI Summary

In point-of-care ultrasound (POCUS), the scarcity of real echocardiographic videos, limited anatomical views, and high inter-operator variability hinder accurate automatic ejection fraction (EF) estimation. To address this, we propose ControlEchoSynth—the first framework leveraging controllable video diffusion models for echocardiogram synthesis. It conditions on real apical two-chamber and four-chamber views to generate temporally coherent, anatomically plausible synthetic echocardiographic videos. These high-fidelity synthetic sequences significantly enhance the robustness and accuracy of EF estimation models. Experiments demonstrate that models trained on our synthetic data outperform those trained with conventional augmentation techniques; notably, in low-data regimes, EF prediction error is reduced by up to 32%. This work establishes a novel paradigm for medical video generation and automated clinical parameter assessment, bridging critical gaps between synthetic data fidelity and downstream diagnostic performance.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• Improving ejection fraction estimation accuracy with synthetic echo views
• Overcoming limited echocardiogram data availability through controlled generation
• Enhancing machine learning models for cardiac assessment using diffusion

Innovation

• Controlled video diffusion for synthetic echo views
• Conditional generative model for EF estimation
• Synthetic data augmentation to boost accuracy