DYNAMO Framework: Advancing non-invasive, rapid calibration in cardiac digital twin technology

Area of Science:

• Cardiovascular Medicine
• Computational Biology
• Biomedical Engineering

Background:

• Atrial arrhythmias pose significant challenges in cardiac electrophysiology.
• Cardiac digital twins (CDTs) offer personalized treatment but face limitations like invasive requirements and long computation times.
• The DYNAMO framework utilizes non-invasive electrocardiographic imaging (ECGI) to create CDTs.

Purpose of the Study:

• To introduce DYNAMO, a novel framework for generating CDTs.
• To leverage ECGI for personalized cardiac modeling, overcoming limitations of existing methods.
• To validate the DYNAMO framework's accuracy and efficiency in simulating cardiac dynamics.

Main Methods:

• DYNAMO employs a reaction-diffusion automata model for reduced computational load.
• ECGI data is calibrated to personalize CDTs by extracting local activation times (LATs) and conduction velocities.
• Validation involved comparison with detailed models and patient data in sinus rhythm (SR) and atrial flutter (AFl), assessing LATs and body surface potential maps (BSPM).

Main Results:

• CDTs generated by DYNAMO accurately replicated cardiac dynamics, with low average LAT differences (3.8 ms for SR, 8.03 ms for AFl).
• High BSPM cross-correlation was achieved for patients (0.89 for SR, 0.75 for AFl).
• The framework demonstrated superior computational efficiency and successfully simulated an atrial flutter ablation outcome.

Conclusions:

• DYNAMO advances personalized cardiac health management through efficient, non-invasive CDT creation.
• The framework's ability to simulate treatment strategies shows promise for improving atrial arrhythmia management.
• This study supports the broader application of ECGI in personalized cardiovascular medicine.