ReviewJournal of the American Heart Association2024
Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.
Review in Journal of the American Heart Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
51 citing papers in PubMed.
- Functionalized biomaterial platforms for interventional embolization therapy of hepatocellular carcinoma.Bioactive materials · 2027Review
- HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing.Computer methods in applied mechanics and engineering · 2026Article
- Toward Precision Cardiac Rehabilitation: Current Limitations and Future Opportunities of Omics and Artificial Intelligence.Sports medicine (Auckland, N.Z.) · 2026Review
- Artificial Intelligence in Pediatric Cardiovascular Genetics: From Multimodal Diagnosis to Risk Prediction and Precision Therapeutics.International journal of molecular sciences · 2026Review
- A Real-Time Digital Twin for Human Cardiovascular Applications.International journal for numerical methods in biomedical engineering · 2026Article
- Healthcare Digital Twins Across Scales: A Narrative Review and Five-Level Conceptual Framework.Healthcare (Basel, Switzerland) · 2026Review
- A modelling study of right ventricular growth with valvular regurgitation.Biomechanics and modeling in mechanobiology · 2026Article
- The Future of Imaging in Heart Failure: Toward Precision Phenotyping, Integration, and Intelligence.Current heart failure reports · 2026Review
- Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Integrating AI-Driven Diagnostics in Arrhythmia Care to Enhance Patient Outcomes: A Narrative Review.Cureus · 2026Review
- Assessing Quality of Life in Genetic Cardiomyopathies: A Scoping Review.International journal of environmental research and public health · 2026Article
- Artificial Intelligence is a Useful Tool in Exercise Science and Sports Medicine.Medicine and science in sports and exercise · 2026Article
- Participatory Digital Twins for Chronic Care: From Predictive Models to Shared Sensemaking.Journal of participatory medicine · 2026Article
- Digital twins and digital models of the human circulatory system.Nature reviews bioengineering · 2026Article
- A narrative review on the use of artificial intelligence in cardiovascular medicine.Cardiovascular diagnosis and therapy · 2026Review
- Cardiovascular digital twins using a Windkessel physics informed neural network.NPJ digital medicine · 2026Article
- Intravascular ultrasound wall shear stress imaging in stented coronary arteries with ultrafast Doppler.Scientific reports · 2026Article
- On the accuracy of implicit neural representations for cardiovascular anatomies and hemodynamic fields.Computers in biology and medicine · 2026Article
- 3d elastic-modulus imaging using ultrasound linear arrays and efficient data-driven training strategies.Biomechanics and modeling in mechanobiology · 2026Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.