AI and Data-Driven Approaches Poised to Bridge the Gap Between Cardiovascular Modeling and Clinical Practice
核心洞察
Cardiovascular diseases (搜索) remain a leading cause of mortality globally, driving demand for more precise diagnostic and predictive tools.
Traditional computational fluid dynamics and fluid-structure interaction models offer deep hemodynamic insights but face high computational costs and limited clinical validation.
Machine learning enables surrogate models and automated image segmentation, accelerating simulations and connecting medical images to numerical simulations for personalized cardiovascular Digital Twins.
Cardiovascular diseases (搜索) remain a leading cause of mortality globally, driving the need for more precise diagnostic and predictive tools. While traditional computational fluid dynamics (CFD) and fluid-structure interaction (FSI) models have provided deep insights into hemodynamic patterns and arterial mechanics relevant to disease diagnosis and mechanisms, their translation into clinical practice has proven challenging due to high computational costs, lack of clinical validation, and the inherent biological variability and limited accessibility of patient-specific data.
Recent advances in Artificial Intelligence (AI) are opening new opportunities to address these challenges. In particular, Machine Learning (ML) techniques are enabling data-driven strategies that accelerate simulations through surrogate or reduced-order models and enhance medical image analysis. By automating image segmentation and extracting high-dimensional features from clinical imaging data, ML helps connect raw medical images and advanced numerical simulations, paving the way toward personalized cardiovascular Digital Twins.
Bridging Physics-Based and Data-Driven Methods
The goal of this emerging research agenda is to showcase the synergy between traditional computational modeling and emerging data-driven technologies. Researchers aim to collect contributions that advance the current state-of-the-art in cardiovascular modeling, addressing key challenges such as improving model building, computational efficiency, refining patient-specific parameter identification, and enhancing model reliability.
By bringing together experts in biomechanics, data science, and clinical imaging, the field intends to record advancements for the future development of integrated frameworks for clinical planning, risk stratification, and the design of cardiovascular devices. Ultimately, the objective is to highlight how the convergence of physics-based and data-driven methods can lead to more robust, interpretable, and clinically useful cardiovascular models.
Arterial Stiffness and the Multi-Omics Frontier
In parallel, cardiovascular health research is increasingly shaped by advances in bioinformatics, artificial intelligence, machine learning, multi-omics, imaging, and digital health. Arterial stiffness (搜索) is recognized as a hallmark of vascular aging (搜索) and an independent predictor of cardiovascular events, cognitive decline, and mortality. At the same time, genomics, transcriptomics, proteomics, metabolomics, imaging, and wearable technologies are generating complex datasets with the potential to clarify disease mechanisms and improve risk assessment.
However, the integration of these data and methods remains fragmented, with persistent challenges involving data heterogeneity, model interpretability, reproducibility, bias, validation, and clinical implementation. Although recent studies have identified promising biomarkers, predictive models, therapeutic targets, and digital phenotypes, further investigation is needed to establish robust, biologically informed, and clinically transferable approaches. Interdisciplinary research combining computational innovation with cardiovascular and vascular biology is therefore essential to advance precision cardiovascular medicine.
Toward Clinically Trustworthy AI
A central focus of ongoing work is whether explainable, reproducible, and clinically validated models can improve early detection, personalize prevention and treatment, and support translation from computational discoveries to meaningful cardiovascular outcomes. Key themes under investigation include biomarker discovery and validation for cardiovascular disease, vascular aging (搜索), and arterial stiffness (搜索); multi-omics integration and omics-based cardiovascular risk stratification; and artificial intelligence, machine learning, and deep learning for vascular phenotyping.
Additional priorities span the prediction of arterial stiffness (搜索), vascular aging (搜索), cardiovascular events, and healthy longevity; network medicine, systems biology, and computational modeling of cardiovascular mechanisms; cardiovascular imaging analytics and automated vascular assessment; and digital phenotyping and wearable technologies for cardiovascular monitoring. The field also emphasizes explainable, interpretable, fair, and clinically trustworthy artificial intelligence, alongside causal inference, target trial emulation, and computational epidemiology.
The scope of this work includes computational and interdisciplinary studies using clinical, epidemiological, molecular, imaging, and digital health data, while emphasizing biological relevance and potential clinical translation. Contributions may address human studies, validated computational methods, or appropriate experimental and translational models, with the overarching aim of advancing precision medicine applications in cardiovascular prevention, diagnosis, and treatment.