Stratifying pulmonary hypertension severity in newborns from multi-view echocardiograms using variational autoencoders

Background Pulmonary hypertension (PH) in newborns is a severe condition characterized by elevated pulmonary artery pressure, leading to right ventricular strain and impaired oxygenation, and significant morbidity and mortality. Right heart catheterization (RHC) is the gold standard for PH diagnosis but is invasive, costly, and associated with procedural risks. Instead, transthoracic echocardiography (ECHO) is widely used for non-invasive PH assessment, although its interpretation is highly operator-dependent. While automated machine learning methods have been proposed for PH detection, existing approaches largely focus on adults and often rely on single-view echocardiographic frames, limiting their generalizability to neonatal populations. Objective We aim to leverage a multi-view variational autoencoder (VAE) framework to predict PH severity in newborns using echocardiographic videos. We hypothesize that learning latent representations across multiple echocardiographic views improves classification performance and generalization compared to traditional models. Methods We analyzed a dataset of 936 transthoracic ECHO videos from 192 newborns (56±160 days old, 2.9±1.5Kg), with a held-out test set of 375 videos from 78 newborns (34±42 days old, 2.1±1.2Kg). Standard ECHO views included PLAX, A4C, and three parasternal short-axis views (PSAX-P, PSAX-S, PSAX-A). PH severity was categorized as none, mild, or moderate-to-severe based on septal flattening patterns. A multi-view VAE framework with a variational mixture-of-experts prior was trained to extract latent representations from ECHO videos, leveraging shared information across different views. Classification performance was compared against single-view and supervised learning from Ragnarsdottir et al. 24’ [1]. The study and method pipeline is illustrated in figure 1. Results Multi-view learning significantly improved PH classification accuracy compared to single-view approaches. Our VAE model achieved an AUROC of 0.83 ± 0.02 and a balanced accuracy of 0.74 ± 0.02 for binary PH detection, outperforming single-view VAEs and traditional supervised learning models. Importantly, our VAE exhibited greater robustness to unseen test data, demonstrating improved generalization across newborn populations. The model effectively distinguished PH severity levels, with the highest performance gains observed in the multi-class classification task (AUROC = 0.74 ± 0.01) –comprehensive results in figure 2. Conclusion Automated PH assessment using echocardiography is essential for early detection and intervention, particularly in neonates where timely diagnosis can impact survival. Our multi-view VAE-based framework enhances PH classification, reduces operator dependency, and improves diagnostic consistency. These improvements could enable more standardized and accessible screening, particularly in resource-limited settings where expert evaluation may not always be available.Study pipeline and method. Summary results (baseline [1]).