Advanced computation in cardiovascular physiology: new challenges and opportunities

Area of Science:

• Computational physiology
• Cardiovascular control
• Autonomic nervous system dynamics

Background:

• Advanced signal processing and AI tools offer diagnostic capabilities in computational physiology.
• The complexity of computational models ('black boxes') limits biological and pathological insight.
• Understanding autonomic nervous system dynamics is crucial for cardiovascular physiology and pathology.

Purpose of the Study:

• To highlight challenges and opportunities in applying advanced computational tools to cardiovascular data.
• To discuss the transition from complex 'black box' models to explainable and personalized clinical models.
• To focus on autonomic nervous system dynamics in cardiovascular control.

Main Methods:

• Development and adaptation of complex signal processing methods.
• Application of multivariate, multiscale, and nonlinear cardiovascular models.
• Utilizing deep and transfer learning algorithms on large datasets.

Main Results:

• Advanced computational tools show promise but raise issues of specificity in heartbeat-related features.
• The limitations of 'black box' models in providing biological insight are evident.
• The need for explainable AI in cardiovascular research is highlighted.

Conclusions:

• A transition to explainable and personalized clinical models is imminent in cardiovascular research.
• Advanced computational approaches require adaptation for deeper scientific inference.
• Integrating complex computational methods with biological understanding is key for future advancements.