ArticleJournal of cardiovascular translational research2023
Machine Learning Identification Framework of Hemodynamics of Blood Flow in Patient-Specific Coronary Arteries with Abnormality.
Article in Journal of cardiovascular translational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Rapid prediction of wall shear stress in stenosed coronary arteries based on deep learning.Frontiers in bioengineering and biotechnology · 2024Article
- Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations.PloS one · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we put forth a new deep neural network framework to predict flow behavior in a coronary arterial network with different properties in the presence of any abnormality like stenosis. An artificial neural network (ANN) model is trained using synthetic data so that it can predict the pressure and velocity within the arterial network. The data required to train the neural network were obtained from the CFD analysis of several geometries of arteries with specific features in ABAQUS software. The proposed approach precisely predicts the hemodynamic behavior of the blood flow. The average accuracy of the pressure prediction was 98.7%, and the average velocity magnitude accuracy was 93.2%. Our model can also be used to predict fractional flow reserve (FFR), which is one of the main indices to determine the severity of stenosis, and our model predicts this index successfully based on the artery features.
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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.