ArticleIEEE transactions on medical imaging2024
Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning.
Article in IEEE transactions on medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Impact of calcifications on paravalvular leakage by transcatheter aortic valve prostheses: findings from a new in silico clinical trial framework.Biomechanics and modeling in mechanobiology · 2025Trial
- HeartSimSage: Attention-Enhanced Graph Neural Networks for Accelerating Cardiac Mechanics Modeling.Journal of computational physics · 2026Article
- Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models.PLoS computational biology · 2026Article
- Dynamic insights into cellular mechanics and membrane undulations in vascular smooth muscle cells.American journal of physiology. Cell physiology · 2026Article
- Investigating the role of structural wall stress in aortic growth prognosis in acute uncomplicated type B aortic dissection.Biomechanics and modeling in mechanobiology · 2025Article
- Innovative Integration of 4D Cardiovascular Reconstruction and Hologram: Framework Development of a New Visualization Tool for Coronary Artery Bypass Grafting Planning.JMIR medical informatics · 2025Article
- Article
- AI-Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Robust automated calcification meshing for personalized cardiovascular biomechanics.NPJ digital medicine · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Automated volumetric meshing of patient-specific heart geometry can help expedite various biomechanics studies, such as post-intervention stress estimation. Prior meshing techniques often neglect important modeling characteristics for successful downstream analyses, especially for thin structures like the valve leaflets. In this work, we present DeepCarve (Deep Cardiac Volumetric Mesh): a novel deformation-based deep learning method that automatically generates patient-specific volumetric meshes with high spatial accuracy and element quality. The main novelty in our method is the use of minimally sufficient surface mesh labels for precise spatial accuracy and the simultaneous optimization of isotropic and anisotropic deformation energies for volumetric mesh quality. Mesh generation takes only 0.13 seconds/scan during inference, and each mesh can be directly used for finite element analyses without any manual post-processing. Calcification meshes can also be subsequently incorporated for increased simulation accuracy. Numerous stent deployment simulations validate the viability of our approach for large-batch analyses. Our code is available at https://github.com/danpak94/Deep-Cardiac-Volumetric-Mesh.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.