Digital vessel for the diagnosis of cardiovascular diseases

Abstract

Over the past few decades, advances in physiological monitoring, imaging technologies, and artificial intelligence (AI) have greatly improved the diagnosis and treatment of cardiovascular diseases. However, traditional diagnostic methods have limitations, particularly in dynamic assessment and personalized care. Digital Twin technology offers a solution by creating virtual replicas of physical entities, enabling precise disease prediction and personalized interventions. This review introduces the concept of the digital vessel, a patient-specific cardiovascular digital twin that integrates imaging data, physiological signals, and AI-driven simulations. We define its core framework, summarize advances in image analysis, computational modeling, and predictive AI, and compare digital vessel models with conventional diagnostic tools in terms of data input, individualization, feedback, and scalability. Finally, we outline key challenges, including data integration, computational cost, clinical validation, and regulatory issues, and propose future research directions. By consolidating current knowledge, this review positions the digital vessel as a pathway toward precision diagnosis and personalized cardiovascular care.

Keywords

Digital vessel / cardiovascular disease / digital twin / artificial intelligence / precision health

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