Machine learning augmented reduced-order models for FFR-prediction-Reference-Cited by-同舟云学术

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Machine learning augmented reduced-order models for FFR-prediction

Published:2021-10 Issue: Volume:384 Page:113892
ISSN:0045-7825
Container-title:Computer Methods in Applied Mechanics and Engineering
language:en
Short-container-title:Computer Methods in Applied Mechanics and Engineering

Author:

Fossan Fredrik E.^ORCID,Müller Lucas O.^ORCID,Sturdy Jacob^ORCID,Bråten Anders T.,Jørgensen Arve^ORCID,Wiseth Rune,Hellevik Leif R.

Funder

Norges teknisk-naturvitenskapelige universitet

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Reference56 articles.

1. Coronary CT angiographic and flow reserve-guided management of patients with stable ischemic heart disease;Nørgaard;J. Am. Coll. Cardiol.,2018

2. Artificial intelligence in precision cardiovascular medicine;Krittanawong;J. Am. Coll. Cardiol.,2017

3. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review;Dey;J. Am. Coll. Cardiol.,2019

4. State-of-the-art deep learning in cardiovascular image analysis;Litjens;JACC: Cardiovasc. Imaging,2019

5. Cardiovascular event prediction by machine learning;Bharath;Circ. Res.,2017

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1. Attention-based multi-fidelity machine learning model for fractional flow reserve assessment;Computer Methods in Applied Mechanics and Engineering;2024-12

2. Towards a reduced order model for EVAR planning and intra-operative navigation;Medical Engineering & Physics;2024-09

3. Artificial Intelligence Analysis of ECG to Determine Fractional Flow Reserve (FFR);2024-08-28

4. Rapid prediction of wall shear stress in stenosed coronary arteries based on deep learning;Frontiers in Bioengineering and Biotechnology;2024-08-12

5. A review of computational methodologies to predict the fractional flow reserve in coronary arteries with stenosis;Journal of Biomechanics;2024-08

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