Hemodynamic Modeling, Medical Imaging, and Machine Learning and Their Applications to Cardiovascular Interventions
Abstract
Cardiovascular disease is a deadly global health crisis that carries a substantial financial burden. Innovative treatment and management of cardiovascular disease straddles medicine, personalized hemodynamic modeling, machine learning, and modern imaging to help improve patient outcomes and reduce the economic impact. Hemodynamic modeling offers a non-invasive method to provide clinicians with new pre- and post- procedural metrics and aid in the selection of treatment options. Medical imaging is an integral part in clinical workflows for understanding and managing cardiac disease and interventions. Coupling machine learning with modeling, and cardiovascular imaging, provides faster modeling, improved data fidelity, and an enhanced understanding and earlier detection of cardiovascular anomalies, leading to the development of patient-specific diagnostic and predictive tools for characterizing and assessing cardiovascular outcomes. Herein, we provide a scoping review of translational hemodynamic modeling, medical imaging, and machine learning and their applications to cardiovascular interventions. We particularly focus on providing an intuitive understanding of each of these approaches and their ability to support decision making during important clinical milestones.
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Simulations provide a controlled, safe, and cost-effective environment for healthcare professionals and AI engineers to prototype, test, and evaluate new medical AI technologies, interventions, and treatments.
“Simulations provide a controlled, safe, and cost-effective environment for healthcare professionals and AI engineers to prototype, test, and evaluate new medical AI technologies, interventions, and treatments (Kadem et al. 2023).”
From Human-Clinical AI Agent Collaboration (2025) · cites this paper as Kadem et al. 2023
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Method
- Authors
- Kadem, M., Garber, L., Abdelkhalek, M., Al-Khazraji, B. K., & Keshavarz-Motamed, Z.
- Affiliations
- McMaster University (CA)
- Department of Mechanical Engineering, School of Biomedical Engineering and School of Computational Science and Engineering, McMaster University, Hamilton, ON, Canada
- Venue
- IEEE Reviews in Biomedical Engineering
- Published
- 2023
- Volume
- 16
- Pages
- 403-423
- ISSN
- 1937-3333 (ISSN-L)
- 1941-1189
- OpenAlex
- W4206835854
- Cited by
- 89
- References
- 0
- Citations by year
- 2026: 23 · 2025: 29 · 2024: 16 · 2023: 17 · 2022: 4
- FWCI
- 2.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Reviews in Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NSERC Discovery (RGPIN-2017-05349)
- NSERC CRD (CRDPJ 537352 -- 18)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- hemodynamic modeling, cardiovascular imaging, machine learning, cardiovascular interventions, patient-specific modeling, treatment selection, cardiovascular disease, clinical decision support, cardiovascular anomalies
- MeSH terms
- Machine Learning, Cardiovascular Diseases, Diagnostic Imaging, Heart Diseases, Humans, Radiography
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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