Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.

Area of Science:

• Cardiovascular Medicine
• Biomedical Engineering
• Computational Science

Background:

• Advances in imaging, genomics, and monitoring have improved cardiovascular disease (CVD) and stroke care.
• Current challenges include processing complex data, mapping disease trajectories, and optimizing interventions.
• Personalized medicine requires integrating diverse data for individual patient management.

Purpose of the Study:

• To review digital twin technology as a solution for personalized cardiovascular medicine.
• To explore how digital twins can address challenges in processing complex medical data and predicting disease progression.
• To highlight the potential of digital twins in optimizing clinical interventions and patient outcomes.

Main Methods:

• Review of recent advances in digital twin technology, scientific computation, AI, and sensor technology.
• Discussion of mechanistic physiological models as foundational elements for digital twins.
• Examination of verification, validation, and uncertainty quantification for clinical trust.

Main Results:

• Digital twins enable bidirectional interaction between physical and virtual counterparts, continuously updating disease projections.
• Mechanistic models within digital twins forecast optimal cardiovascular health management using individualized data.
• Existing literature provides exemplars of mechanistic model development for cardiovascular dynamics.

Conclusions:

• Digital twin technology holds significant promise for personalized cardiovascular medical practice.
• Integrating mechanistic models and advanced computation can create robust digital twins for CVD management.
• Further research is needed to address technical challenges and fully establish digital twins in clinical settings.