Echo-Path: Pathology-Conditioned Echo Video Generation
This work addresses a data scarcity problem in medical imaging for cardiovascular disease diagnosis, offering a domain-specific solution that is incremental but with practical gains.
The paper tackled the scarcity of echocardiographic data for certain cardiac pathologies by proposing Echo-Path, a generative framework that synthesizes realistic ultrasound videos conditioned on specific diseases like atrial septal defect and pulmonary arterial hypertension, resulting in improved diagnosis accuracy by 7% and 8% respectively when used for data augmentation.
Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, and echocardiography is critical for diagnosis of both common and congenital cardiac conditions. However, echocardiographic data for certain pathologies are scarce, hindering the development of robust automated diagnosis models. In this work, we propose Echo-Path, a novel generative framework to produce echocardiogram videos conditioned on specific cardiac pathologies. Echo-Path can synthesize realistic ultrasound video sequences that exhibit targeted abnormalities, focusing here on atrial septal defect (ASD) and pulmonary arterial hypertension (PAH). Our approach introduces a pathology-conditioning mechanism into a state-of-the-art echo video generator, allowing the model to learn and control disease-specific structural and motion patterns in the heart. Quantitative evaluation demonstrates that the synthetic videos achieve low distribution distances, indicating high visual fidelity. Clinically, the generated echoes exhibit plausible pathology markers. Furthermore, classifiers trained on our synthetic data generalize well to real data and, when used to augment real training sets, it improves downstream diagnosis of ASD and PAH by 7\% and 8\% respectively. Code, weights and dataset are available here https://github.com/Marshall-mk/EchoPathv1