Computers in biology and medicine · 2025
DYNAMO Framework: Advancing non-invasive, rapid calibration in cardiac digital twin technology.
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Herrero-Martín Clara, Molero Rubén, Sánchez Jorge, Reventós-Presmanes Jana, Guichard Jean-Baptiste, Althoff Till, Porta-Sánchez Andreu, Roca-Luque Ivo, Mont Lluis, Guillem María S, Climent Andreu M, Hernández-Romero Ismael
Аннотация
INTRODUCTION: Atrial arrhythmias management remains a significant challenge in cardiac electrophysiology. Cardiac digital twins (CDTs) offer a promising solution for individualized treatment. However, existing models are limited by invasive input requirements or long computational times. This paper introduces DYNAMO, a framework that leverages non-invasive electrocardiographic imaging (ECGI) to create CDTs.
METHODS: DYNAMO utilizes a reaction-diffusion automata model, reducing computational demands while capturing personalized cardiac dynamics using ECGI data. The calibration process maps ECGI data into CDTs by extracting key parameters for personalization, specifically LATs and conduction velocities. The framework was validated against detailed models and patient data in sinus rhythm (SR) and atrial flutter (AFl), comparing local activation times (LATs) and body surface potential maps (BSPM) for models, and focusing on BSPM for patients.
RESULTS: In-silico and clinical validation demonstrated the effectiveness of the framework. CDTs closely replicated cardiac dynamics, with an average LAT difference of 3.8 ms for SR models and 8.03 ms for AFl. For patients, CDTs achieved a high BSPM cross-correlation, with values averaging 0.89 for SR and 0.75 for AFl, and successfully replicated the ablation outcome in an AFl scenario. The framework also demonstrated superior computational efficiency compared to traditional models for equivalent simulations.
CONCLUSIONS: DYNAMO represents a significant advancement in personalized cardiac health management. Using non-invasive ECGI to create CDTs offers an efficient approach to cardiac modeling. Its capability to simulate treatment strategies holds promise for improving clinical outcomes in atrial arrhythmia management. This study lays the basis for the broader application of ECGI in personalized medicine.
METHODS: DYNAMO utilizes a reaction-diffusion automata model, reducing computational demands while capturing personalized cardiac dynamics using ECGI data. The calibration process maps ECGI data into CDTs by extracting key parameters for personalization, specifically LATs and conduction velocities. The framework was validated against detailed models and patient data in sinus rhythm (SR) and atrial flutter (AFl), comparing local activation times (LATs) and body surface potential maps (BSPM) for models, and focusing on BSPM for patients.
RESULTS: In-silico and clinical validation demonstrated the effectiveness of the framework. CDTs closely replicated cardiac dynamics, with an average LAT difference of 3.8 ms for SR models and 8.03 ms for AFl. For patients, CDTs achieved a high BSPM cross-correlation, with values averaging 0.89 for SR and 0.75 for AFl, and successfully replicated the ablation outcome in an AFl scenario. The framework also demonstrated superior computational efficiency compared to traditional models for equivalent simulations.
CONCLUSIONS: DYNAMO represents a significant advancement in personalized cardiac health management. Using non-invasive ECGI to create CDTs offers an efficient approach to cardiac modeling. Its capability to simulate treatment strategies holds promise for improving clinical outcomes in atrial arrhythmia management. This study lays the basis for the broader application of ECGI in personalized medicine.