What does this research mean for the field?

Mathematical modeling, integrating fluid dynamics, computational mechanics, and artificial intelligence, enables the generation of highly accurate, patient-specific digital twins of the cardiovascular system for clinical application. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To provide a comprehensive overview of mathematical frameworks, fluid-structure interaction mechanics, and computational methods used to model cardiovascular dynamics and patient-specific heart function.

September 5, 2026Open Access

Mathematical modeling of cardiovascular fluid dynamics and electromechanics enables personalized digital twins for clinical use.

Q: What is the clinical evidence from this study?

Study design: Review. Population: Cardiovascular system hemodynamics. Intervention: Mathematical and physical modeling.

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Design

Review

Key result

Mathematical modeling of the cardiovascular system, including fluid dynamics and electromechanics, enables the creation of personalized digital twins for clinical application.

Authors

ECEnrico Catalano

Discussion

Member takes

Overview

Personalized digital twins may guide future cardiovascular interventions; extends multiscale models but leaves open prospective clinical validation.

Key Points

To provide a comprehensive overview of mathematical frameworks, fluid-structure interaction mechanics, and computational methods used to model cardiovascular dynamics and patient-specific heart function.
Reviewed governing physical equations, including Navier-Stokes formulations and non-Newtonian rheological models for vascular blood flow.
Analyzed structural and fluid-structure interaction frameworks, such as the Arbitrary Lagrangian-Eulerian approach, geometrical multiscale (3D-1D-0D) networks, and the Holzapfel-Ogden hyperelastic model.
Evaluated the integration of artificial intelligence and scientific machine learning algorithms for accelerating complex cardiovascular simulations.
Multiscale and fluid-structure interaction models accurately capture localized hemodynamic stress alterations associated with pathological vascular remodeling and aneurysm formation.
Whole-heart electromechanical and hyperelastic models successfully simulate organ-level contraction, passive myocardial properties, and heart valve mechanics.
Scientific machine learning significantly reduces computational latency, enabling the real-time generation of personalized cardiovascular digital twins for clinical use.

PICO

Population

Cardiovascular system hemodynamics

Intervention / Comparator

Mathematical and physical modeling

This overview highlights the potential of mathematical modeling and AI to create personalized digital twins of the cardiovascular system for clinical applications.

Cite This Study

Enrico Catalano (2026) conducted a review in Cardiovascular system hemodynamics. Mathematical and physical modeling was evaluated. Mathematical modeling of the cardiovascular system, including fluid dynamics and electromechanics, enables the creation of personalized digital twins for clinical application.

synapsesocial.com/papers/6a9bd4436b95aff0620ebd67 https://doi.org/10.5281/zenodo.22284691

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September 5, 2026Open Access

Mathematical modeling of cardiovascular fluid dynamics and electromechanics enables personalized digital twins for clinical use.

View Full Paper

Ask AI

Bookmark

Design

Review

Key result

Mathematical modeling of the cardiovascular system, including fluid dynamics and electromechanics, enables the creation of personalized digital twins for clinical application.

Authors

ECEnrico Catalano

Discussion

Member takes

Overview

Personalized digital twins may guide future cardiovascular interventions; extends multiscale models but leaves open prospective clinical validation.

Key Points

To provide a comprehensive overview of mathematical frameworks, fluid-structure interaction mechanics, and computational methods used to model cardiovascular dynamics and patient-specific heart function.
Reviewed governing physical equations, including Navier-Stokes formulations and non-Newtonian rheological models for vascular blood flow.
Analyzed structural and fluid-structure interaction frameworks, such as the Arbitrary Lagrangian-Eulerian approach, geometrical multiscale (3D-1D-0D) networks, and the Holzapfel-Ogden hyperelastic model.
Evaluated the integration of artificial intelligence and scientific machine learning algorithms for accelerating complex cardiovascular simulations.
Multiscale and fluid-structure interaction models accurately capture localized hemodynamic stress alterations associated with pathological vascular remodeling and aneurysm formation.
Whole-heart electromechanical and hyperelastic models successfully simulate organ-level contraction, passive myocardial properties, and heart valve mechanics.
Scientific machine learning significantly reduces computational latency, enabling the real-time generation of personalized cardiovascular digital twins for clinical use.

PICO

Population

Cardiovascular system hemodynamics

Intervention / Comparator

Mathematical and physical modeling

This overview highlights the potential of mathematical modeling and AI to create personalized digital twins of the cardiovascular system for clinical applications.

Cite This Study

synapsesocial.com/papers/6a9bd4436b95aff0620ebd67 https://doi.org/10.5281/zenodo.22284691

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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