Cardiac image modelling: Breadth and depth in heart disease.

Area of Science:

• Cardiology and Computational Biology
• Medical Imaging and Big Data Analytics

Background:

• Large-scale imaging studies and big health data analytics are advancing the understanding of heart disease mechanisms.
• Computational analysis of cardiac remodelling and physiology offers new avenues for disease characterization.

Discussion:

• Emerging fields include computational analysis of cardiac remodelling and physiology from non-invasive imaging.
• Large cohort studies utilize non-invasive imaging to track heart disease progression from asymptomatic to clinical stages.
• Patient-specific computational modeling enables determination of myocardial tissue biophysical properties.

Key Insights:

• Big health data and machine learning enhance understanding of heart disease.
• Computational modeling can quantify population variation and disease development.
• Non-invasive imaging combined with computational methods allows for patient-specific analysis of cardiac function.

Outlook:

• Open benchmarks, data sharing, and algorithm validation are essential for advancing computational modeling.
• Demonstrating clinical efficacy is key for integrating these methods into patient care.
• Combined population and patient-specific modeling will yield new insights into heart failure, congenital heart disease, and myocardial infarction.