A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data

Area of Science:

• Computational biology
• Cardiac electrophysiology
• Medical modeling

Background:

• Computational models of cardiac electrophysiology have advanced significantly.
• Personalised models aim to improve patient-specific therapy guidance and treatment outcomes.
• Current clinical approaches often rely on population-based data, limiting optimal treatment planning.

Purpose of the Study:

• To report techniques for personalising cardiac computational models.
• To focus on calibrating cardiac tissue conductivity using electroanatomical mapping data.
• To address limitations in current in silico treatment methods.

Main Methods:

• Utilisation of electroanatomical mapping data for model calibration.
• Investigating various activation mapping and calibration methods.
• Developing therapy simulation pipelines for personalised models.

Main Results:

• Techniques for personalising cardiac computational models are presented.
• Focus on calibrating tissue conductivity based on electroanatomical mapping data.
• Exploration of methods to overcome clinical limitations in model generation and validation.

Conclusions:

• Personalised cardiac electrophysiology models hold promise for optimal treatment planning.
• Calibration of tissue conductivity is a key step in creating clinically relevant in silico treatments.
• Further research is needed to refine methods and validate recovered tissue parameters.