ArticleFrontiers in physiology2021
Deep Learning Framework for Real-Time Estimation of
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Left atrial flow and thrombosis risk from 4D CT contrast dynamics by physics-informed neural network and indicator dilution theory.bioRxiv : the preprint server for biology · 2026Article
- Imaging-defined residual risk after left atrial appendage occlusion: from device surveillance to antithrombotic and reintervention decision considerations.Frontiers in cardiovascular medicine · 2026Review
- Rapid wall shear stress prediction for aortic aneurysms using deep learning: a fast alternative to CFD.Medical & biological engineering & computing · 2025Article
- Hemodynamics affects factor XI/XII anticoagulation efficacy in patient-derived left atrial models.Computer methods and programs in biomedicine · 2025Article
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Super-resolution left ventricular flow and pressure mapping by Navier-Stokes-informed neural networks.Computers in biology and medicine · 2025Article
- Developing a nearly automated open-source pipeline for conducting computational fluid dynamics simulations in anterior brain vasculature: a feasibility study.Scientific reports · 2024Article
- Article
- A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data.Scientific reports · 2024Article
- Super-resolution Left Ventricular Flow and Pressure Mapping by Navier-Stokes-Informed Neural Networks.bioRxiv : the preprint server for biology · 2024Article
- The impact of contrast retention on thrombus formation risks in patients with atrial fibrillation: A numerical study.Heliyon · 2024Article
- The role of the pulmonary veins on left atrial flow patterns and thrombus formation.Scientific reports · 2024Article
- A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data.bioRxiv : the preprint server for biology · 2024Article
- Article
- Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes.Journal of clinical medicine · 2023Review
- Increased Blood Residence Time as Markers of High-Risk Patent Foramen Ovale.Translational stroke research · 2023Article
- Article
- Adversarial-based latent space alignment network for left atrial appendage segmentation in transesophageal echocardiography images.Frontiers in cardiovascular medicine · 2023Article
- Machine Learning and the Conundrum of Stroke Risk Prediction.Arrhythmia & electrophysiology review · 2023Review
- An artificial intelligence-based platform for automatically estimating time-averaged wall shear stress in the ascending aorta.European heart journal. Digital health · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patient-specific computational fluid dynamics (CFD) simulations can provide invaluable insight into the interaction of left atrial appendage (LAA) morphology, hemodynamics, and the formation of thrombi in atrial fibrillation (AF) patients. Nonetheless, CFD solvers are notoriously time-consuming and computationally demanding, which has sparked an ever-growing body of literature aiming to develop surrogate models of fluid simulations based on neural networks. The present study aims at developing a deep learning (DL) framework capable of predicting the endothelial cell activation potential (ECAP), an
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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.