ArticleCommunications biology2021
Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 119 citations in OpenAlex.
- The Role of the Cardiothoracic Surgeon in the Age of AI-Are the Robots Going to Take Our Jobs?Medical sciences (Basel, Switzerland) · 2026Pooled it
- Non-invasive Computational Techniques for Diagnosing Myocardial Ischemia: Challenges and Future of FFRAnnals of biomedical engineering · 2026Review
- Beyond CABG vs. PCI: Contemporary and Future Coronary Revascularisation from Historical Evolution to Artificial Intelligence, Robotics, and Hybrid Strategies.Journal of clinical medicine · 2026Article
- Between Hearts and Gears: Technology at the Service of Life.Brazilian journal of cardiovascular surgery · 2026Article
- Postoperative complications of coronary artery bypass grafting: a narrative review on pathophysiology, management strategies, and the emerging role of artificial intelligence.Cardiovascular diagnosis and therapy · 2026Review
- Investigating the early diagnostic value of popliteal artery wall shear stress in lower extremity arterial disease in type 2 diabetes patients using color Doppler ultrasound combined with WSS quantitative analysis.Biomedical engineering online · 2026Article
- Physics constrained graph neural network for real time prediction of intracranial aneurysm hemodynamics.NPJ digital medicine · 2026Article
- Investigation of the physical driving mechanisms of wind noise in hearing devices by computational fluid dynamics.Scientific reports · 2025Article
- Artificial Intelligence in Coronary Artery Interventions: Preprocedural Planning and Procedural Assistance.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Machine learning-based prediction of hemodynamic parameters in left coronary artery bifurcation: A CFD approach.Heliyon · 2025Article
- Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future.Clinics and practice · 2025Review
- Beyond visual inspection: the deep learning revolution in quantitative cerebrovascular imaging.Frontiers in neuroscience · 2025Review
- ML-ROM wall shear stress prediction in patient-specific vascular pathologies under a limited clinical training data regime.PloS one · 2025Article
- Prediction of time averaged wall shear stress distribution in coronary arteries' bifurcation varying in morphological features via deep learning.Frontiers in physiology · 2025Article
- Integrated multidisciplinary approach to aneurysm hemodynamic analysis: numerical simulation,Frontiers in bioengineering and biotechnology · 2025Review
- Active Machine Learning for Pre-procedural Prediction of Time-Varying Boundary Condition After Fontan Procedure Using Generative Adversarial Networks.Annals of biomedical engineering · 2025Article
- Developing a nearly automated open-source pipeline for conducting computational fluid dynamics simulations in anterior brain vasculature: a feasibility study.Scientific reports · 2024Article
- Predicting coronary artery occlusion risk from noninvasive images by combining CFD-FSI, cGAN and CNN.Scientific reports · 2024Article
- Investigation of Relationship between Hemodynamic and Morphometric Characteristics of Aortas in Pediatric Patients.Journal of clinical medicine · 2024Article
- Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data.PLoS computational biology · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.
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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.