Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond.

Area of Science:

• Cardiovascular Biomechanics
• Computational Biology
• Machine Learning Applications

Background:

• Machine learning (ML) and computational power offer new avenues in cardiovascular modeling.
• While ML excels in patient outcome classification and image segmentation, its application in predicting biomechanics like blood flow is nascent.
• Existing physics-based models are well-established for cardiovascular biomechanics.

Purpose of the Study:

• To discuss challenges of using ML to replace physics-based models in cardiovascular biomechanics.
• To explore strategic integration of ML to augment traditional modeling approaches.
• To define the role and interpretation of ML in translational cardiovascular modeling.

Main Methods:

• Perspective article discussing challenges and opportunities.
• Analysis of input feature landscape and high-dimensional output spaces in 3D patient-specific modeling.
• Evaluation of the accuracy-speedup tradeoff for ML models.

Main Results:

• ML faces challenges in replacing physics-based models due to complex input/output spaces.
• Defining the end purpose and interpreting ML model tradeoffs are crucial for translational modeling.
• ML can augment physics-based models by solving ill-defined problems, improving data quality, and analyzing complex data.

Conclusions:

• ML should be strategically integrated as a tool, not a replacement, in cardiovascular biomechanics.
• This integration can enhance modeling by addressing computational expense and data interpretation.
• Focusing on augmenting physics-based models with ML offers significant potential for advancing cardiovascular research.