Translating Mechanistic Insights Into Action and Revealing New Pathways: Machine Learning Approaches in Heart Failure...

Area of Science:

• Cardiology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Heart failure with preserved ejection fraction (HFpEF) is a common condition with high morbidity and mortality.
• Limited treatment options exist for HFpEF, necessitating deeper mechanistic understanding.
• Advanced imaging and machine learning (ML) show promise in addressing these challenges.

Purpose of the Study:

• To review how integrating ML with advanced cardiac imaging can improve understanding of HFpEF.
• To highlight ML's role in identifying phenotypes, biomarkers, and mechanisms of HFpEF.
• To discuss the potential for ML-driven precision medicine in HFpEF treatment.

Main Methods:

• Application of supervised, unsupervised, and reinforcement learning to cardiac imaging data.
• Utilizing deep convolutional neural networks for feature extraction and learning.
• Employing clustering algorithms for automated detection of myocardial characteristics.
• Leveraging multimodal ML frameworks like multifidelity physics-informed neural networks.

Main Results:

• ML techniques can identify HFpEF phenotypes and extract relevant biomarkers from cardiac imaging.
• Integration of advanced imaging and ML provides insights into myocardial stiffness, steatosis, and energetics.
• Automated detection of myocardial fibrosis and other mechanisms is achievable through ML algorithms.
• Multimodal ML frameworks enhance phenotype clustering and enable patient-specific interventions.

Conclusions:

• Integrating ML with advanced imaging is crucial for advancing precision medicine in HFpEF.
• ML approaches can overcome traditional challenges in understanding HFpEF mechanisms.
• This integration guides the development of targeted therapies for improved HFpEF patient outcomes.