Hemodynamic Modeling, Medical Imaging, and Machine Learning and Their Applications to Cardiovascular Interventions-Reference-Cited by-同舟云学术

Hemodynamic Modeling, Medical Imaging, and Machine Learning and Their Applications to Cardiovascular Interventions

Published:2023 Issue: Volume:16 Page:403-423
ISSN:1937-3333
Container-title:IEEE Reviews in Biomedical Engineering
language:
Short-container-title:IEEE Rev. Biomed. Eng.

Author:

Kadem Mason¹^ORCID,Garber Louis¹^ORCID,Abdelkhalek Mohamed¹^ORCID,Al-Khazraji Baraa K.²^ORCID,Keshavarz-Motamed Zahra³^ORCID

Affiliation:

1. School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada

2. Department of Kinesiology and School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada

3. Department of Mechanical Engineering, School of Biomedical Engineering and School of Computational Science and Engineering, McMaster University, Hamilton, ON, Canada

Funder

NSERC Discovery

NSERC CRD

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/4664312/10007429/09678069.pdf?arnumber=9678069

Reference187 articles.

1. Effects of Choice of Medical Imaging Modalities on a Non-invasive Diagnostic and Monitoring Computational Framework for Patients With Complex Valvular, Vascular, and Ventricular Diseases Who Undergo Transcatheter Aortic Valve Replacement

2. A review on low-dimensional physics-based models of systemic arteries: application to estimation of central aortic pressure

3. Non-invasive Assessment of Systolic and Diastolic Cardiac Function During Rest and Stress Conditions Using an Integrated Image-Modeling Approach

4. Ventricular stroke work and vascular impedance refine the characterization of patients with aortic stenosis

5. A Method to Personalize the Lumped Parameter Model of Coronary Artery

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