ReviewAnnals of biomedical engineering2022
Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond.
Review in Annals of biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling.Advanced healthcare materials · 2026Review
- Physics constrained graph neural network for real time prediction of intracranial aneurysm hemodynamics.NPJ digital medicine · 2026Article
- Article
- AI-powered automated model construction for patient-specific CFD simulations of aortic flows.Science advances · 2025Article
- Uncover Hidden Physical Information of Soft Matter by Observing Large Deformation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A novel computational method to predict hypoattenuated leaflet thickening post-transcatheter aortic valve replacement using preprocedural computed tomography scans.JTCVS structural and endovascular · 2025Article
- Machine learning-based prediction of carotid intima-media thickness progression: a three-year prospective cohort study.Frontiers in medicine · 2025Article
- FEBio FINESSE: An Open-Source Finite Element Simulation Approach to Estimate In Vivo Heart Valve Strains Using Shape Enforcement.Annals of biomedical engineering · 2025Article
- Active Machine Learning for Pre-procedural Prediction of Time-Varying Boundary Condition After Fontan Procedure Using Generative Adversarial Networks.Annals of biomedical engineering · 2025Article
- Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach.Artificial intelligence in medicine · 2024Article
- Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials.Cardiovascular engineering and technology · 2024Review
- Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning.Journal of biomechanical engineering · 2024Article
- Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research.American journal of physiology. Heart and circulatory physiology · 2024Review
- A Multidisciplinary Approach to Patient-Specific Surgical Planning in Congenital Heart Disease.JACC. Advances · 2024Article
- Virtual Shape-Editing of Patient-Specific Vascular Models Using Regularized Kelvinlets.IEEE transactions on bio-medical engineering · 2024Article
- Accelerated simulation methodologies for computational vascular flow modelling.Journal of the Royal Society, Interface · 2024Review
- Rapid prediction of wall shear stress in stenosed coronary arteries based on deep learning.Frontiers in bioengineering and biotechnology · 2024Article
- Synergistic Integration of Deep Neural Networks and Finite Element Method with Applications of Nonlinear Large Deformation Biomechanics.Computer methods in applied mechanics and engineering · 2023Article
- A Doppler-exclusive non-invasive computational diagnostic framework for personalized transcatheter aortic valve replacement.Scientific reports · 2023Article
- USING CONVOLUTIONAL NEURAL NETWORK-BASED SEGMENTATION FOR IMAGE-BASED COMPUTATIONAL FLUID DYNAMICS SIMULATIONS OF BRAIN ANEURYSMS: INITIAL EXPERIENCE IN AUTOMATED MODEL CREATION.Journal of mechanics in medicine and biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Recent progress in machine learning (ML), together with advanced computational power, have provided new research opportunities in cardiovascular modeling. While classifying patient outcomes and medical image segmentation with ML have already shown significant promising results, ML for the prediction of biomechanics such as blood flow or tissue dynamics is in its infancy. This perspective article discusses some of the challenges in using ML for replacing well-established physics-based models in cardiovascular biomechanics. Specifically, we discuss the large landscape of input features in 3D patient-specific modeling as well as the high-dimensional output space of field variables that vary in space and time. We argue that the end purpose of such ML models needs to be clearly defined and the tradeoff between the loss in accuracy and the gained speedup carefully interpreted in the context of translational modeling. We also discuss several exciting venues where ML could be strategically used to augment traditional physics-based modeling in cardiovascular biomechanics. In these applications, ML is not replacing physics-based modeling, but providing opportunities to solve ill-defined problems, improve measurement data quality, enable a solution to computationally expensive problems, and interpret complex spatiotemporal data by extracting hidden patterns. In summary, we suggest a strategic integration of ML in cardiovascular biomechanics modeling where the ML model is not the end goal but rather a tool to facilitate enhanced modeling.
Indexed as
Identifiers
35445297What OpenQuestion holds
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.