Cardiovascular models for personalised medicine: Where now and where next?

Area of Science:

• Biomedical Engineering
• Computational Science
• Digital Health

Background:

• Digital twin technology is a key trend in engineering, integrating monitoring and simulation.
• Cardiovascular modeling is evolving towards a more systematic, multi-disciplinary approach.

Purpose of the Study:

• To overview the current state of cardiovascular modeling.
• To discuss challenges and processes for wider adoption in personal health and clinical practice.

Main Methods:

• Integrating physics, mathematics, control theory, AI, and machine learning.
• Enhancing collaboration between modeling and clinical communities.
• Developing model-based understanding for improved physiological measurements.

Main Results:

• Progress in physiological modeling, personalization, and uncertainty quantification.
• Exploration of models for clinical decision support.
• Identification of future steps and challenges in cardiovascular modeling.

Conclusions:

• Wider exploitation of cardiovascular modeling requires addressing challenges in integration and personalization.
• Digital twins and advanced simulation techniques are crucial for future advancements.
• Closer collaboration between engineers and clinicians is essential for clinical translation.