# AI-powered multimodal modeling of personalized hemodynamics in aortic stenosis

arXiv: https://arxiv.org/abs/2407.00535 · HTML: https://mlsanity.com/paper/2407.00535/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 73 references that are in the map.
- First posted on arXiv 29 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [Learning Whole Heart Mesh Generation From Patient Images For Computational Simulations](https://mlsanity.com/paper/2203.10517/) (2022)
- [Robust automated calcification meshing for biomechanical cardiac digital twins](https://mlsanity.com/paper/2403.04998/) (2024)

## Built on by (most cited first, 2 of 2)

- [From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction](https://mlsanity.com/paper/2505.03599/) (2025)
- [Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing](https://mlsanity.com/paper/2506.08280/) (2025)

## Abstract (arXiv)

Aortic stenosis (AS) is the most common valvular heart disease in developed countries. High-fidelity preclinical models can improve AS management by enabling therapeutic innovation, early diagnosis, and tailored treatment planning. However, their use is currently limited by complex workflows necessitating lengthy expert-driven manual operations. Here, we propose an AI-powered computational framework for accelerated and democratized patient-specific modeling of AS hemodynamics from computed tomography. First, we demonstrate that our automated meshing algorithms can generate task-ready geometries for both computational and benchtop simulations with higher accuracy and 100 times faster than existing approaches. Then, we show that our approach can be integrated with fluid-structure interaction and soft robotics models to accurately recapitulate a broad spectrum of clinical hemodynamic measurements of diverse AS patients. The efficiency and reliability of these algorithms make them an ideal complementary tool for personalized high-fidelity modeling of AS biomechanics, hemodynamics, and treatment planning.