Predicting Pulmonary Hypertension in Newborns: A Multi-view VAE Approach
This work addresses the challenge of accurate and non-invasive diagnosis of pulmonary hypertension in newborns, which is critical for timely treatment but often subjective with current methods, representing an incremental improvement in automated detection.
The paper tackled the problem of predicting pulmonary hypertension in newborns using echocardiography by developing a multi-view variational autoencoder model, which achieved improved generalization and classification accuracy compared to single-view and supervised learning approaches.
Pulmonary hypertension (PH) in newborns is a critical condition characterized by elevated pressure in the pulmonary arteries, leading to right ventricular strain and heart failure. While right heart catheterization (RHC) is the diagnostic gold standard, echocardiography is preferred due to its non-invasive nature, safety, and accessibility. However, its accuracy highly depends on the operator, making PH assessment subjective. While automated detection methods have been explored, most models focus on adults and rely on single-view echocardiographic frames, limiting their performance in diagnosing PH in newborns. While multi-view echocardiography has shown promise in improving PH assessment, existing models struggle with generalizability. In this work, we employ a multi-view variational autoencoder (VAE) for PH prediction using echocardiographic videos. By leveraging the VAE framework, our model captures complex latent representations, improving feature extraction and robustness. We compare its performance against single-view and supervised learning approaches. Our results show improved generalization and classification accuracy, highlighting the effectiveness of multi-view learning for robust PH assessment in newborns.