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[2602.23035] Learning Disease-Sensitive Latent Interaction Graphs From Noisy Cardiac Flow Measurements
Summary
This paper presents a novel framework for modeling cardiac blood flow patterns using disease-sensitive latent interaction graphs, enhancing the understanding of cardiac conditions.
Why It Matters
Understanding cardiac flow dynamics is crucial for diagnosing and treating heart diseases. This research introduces a new approach that integrates physics and machine learning to improve the interpretability of cardiac conditions, potentially leading to better clinical outcomes.
Key Takeaways
- Introduces a physics-informed model for cardiac flow analysis.
- Demonstrates strong correlation between graph entropy and disease severity.
- Applies the model to both simulations and real ultrasound datasets.
- Highlights the potential for cross-modal generalization in cardiac assessments.
- Offers interpretable markers for evaluating cardiac disease and interventions.
Computer Science > Machine Learning arXiv:2602.23035 (cs) [Submitted on 26 Feb 2026] Title:Learning Disease-Sensitive Latent Interaction Graphs From Noisy Cardiac Flow Measurements Authors:Viraj Patel, Marko Grujic, Philipp Aigner, Theodor Abart, Marcus Granegger, Deblina Bhattacharjee, Katharine Fraser View a PDF of the paper titled Learning Disease-Sensitive Latent Interaction Graphs From Noisy Cardiac Flow Measurements, by Viraj Patel and 6 other authors View PDF HTML (experimental) Abstract:Cardiac blood flow patterns contain rich information about disease severity and clinical interventions, yet current imaging and computational methods fail to capture underlying relational structures of coherent flow features. We propose a physics-informed, latent relational framework to model cardiac vortices as interacting nodes in a graph. Our model combines a neural relational inference architecture with physics-inspired interaction energy and birth-death dynamics, yielding a latent graph sensitive to disease severity and intervention level. We first apply this to computational fluid dynamics simulations of aortic coarctation. Learned latent graphs reveal that as the aortic radius narrows, vortex interactions become stronger and more frequent. This leads to a higher graph entropy, correlating monotonically with coarctation severity ($R^2=0.78$, Spearman $|\rho|=0.96$). We then extend this method to ultrasound datasets of left ventricles under varying levels of left ventricular as...
