JMIR Publications

ABSTRACT

Background:

Digital twin (DT) systems are emerging as transformative tools in precision cardiology, enabling dynamic, patient-specific simulation for diagnosis and treatment planning. However, the current landscape of DT development, clinical adoption, validation, and implementation remains fragmented and lacks standardization.

Objective:

This systematic review aims to synthesize the current state of cardiovascular digital twin research by addressing eleven research questions spanning modeling technologies, data infrastructure, clinical applications, clinical impact, implementation barriers, and ethical considerations.

Methods:

Following PRISMA 2020 guidelines, we systematically screened five major databases and identified 270 records. After applying predefined eligibility criteria, 42 original research articles were included in the final synthesis. Structured extraction was conducted using eleven thematic research questions and controlled vocabularies.

Results:

Hybrid models combining mechanistic and data-driven methods were the most common modeling approach. Electromechanical and wavefront propagation models dominated mechanistic designs, while deep learning and optimization methods were frequent in AI applications. Data integration spanned 12 modalities, with imaging, electrical signals, and omics data most used. Static visualizations were common; interactive outputs were rare. Clinically, digital twins supported diagnosis, simulation, therapy planning, and monitoring, most often applied to arrhythmias and heart failure. Reported impacts included improved accuracy, personalization, and procedural outcomes. Key barriers included computational cost, data gaps, and lack of interoperability. Ethical concerns centered on data privacy, transparency, and consent.

Conclusions:

Hybrid models combining mechanistic and data-driven methods were the most common modeling approach. Electromechanical and wavefront propagation models dominated mechanistic designs, while deep learning and optimization methods were frequent in AI applications. Data integration spanned 12 modalities, with imaging, electrical signals, and omics data most used. Static visualizations were common; interactive outputs were rare. Clinically, digital twins supported diagnosis, simulation, therapy planning, and monitoring, most often applied to arrhythmias and heart failure. Reported impacts included improved accuracy, personalization, and procedural outcomes. Key barriers included computational cost, data gaps, and lack of interoperability. Ethical concerns centered on data privacy, transparency, and consent.