ArticleHypertension (Dallas, Tex. : 1979)2024
Vascular Age Assessed From an Uncalibrated, Noninvasive Pressure Waveform by Using a Deep Learning Approach: The AI-VascularAge Model.
Article in Hypertension (Dallas, Tex. : 1979), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 19 citations in OpenAlex.
- Associations of Brain Structure and Neuropsychological Function With Artificial Intelligence Estimates of Biological Vascular Age.Arteriosclerosis, thrombosis, and vascular biology · 2026Article
- Exploring hemodynamic measurements from the Tromsø Study for prediction of cardiovascular disease using traditional statistical models and machine learning approaches.Scientific reports · 2026Article
- Development and Validation of the AI-HeartAge Model in Framingham and UK Biobank.Hypertension (Dallas, Tex. : 1979) · 2026Article
- Vascular Age: A narrative review of assessment methods, clinical applications, and future directions.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Optimization of artificial intelligence models for prediction of new-onset cardiovascular disease in patients with arterial hypertension.PLOS digital health · 2026Article
- A curated arterial stiffness dataset for vascular age prediction in China.Scientific data · 2026Article
- Integrating emerging vascular biomarkers into clinical hypertension practice: Reflections on the hypertension research update and perspectives 2025.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- Vascular age estimation using a consumer wearable sleep tracker.PLOS digital health · 2026Article
- Recent advances and emerging perspectives in vascular and cardiovascular research: A 2025 update.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Review
- Smartphone Measurement of Aortic Arch Pulse-Wave Velocity and Total Arterial Compliance: Accessible Local and Global Arterial Stiffness Assessment.Journal of the American Heart Association · 2026Observational
- Association of Vascular Age and Subclinical Target Organ Damage in a Beijing Community-Based Population: A Cross-Sectional Study.Journal of cardiovascular development and disease · 2026Article
- Machine learning assessment of retinal blood flow links metabolic dysfunction and accelerated microvascular aging.Scientific reports · 2026Article
- Relations of Artificial Intelligence Vascular Age With Cardiometabolic Disease Progression: The Framingham Heart Study.Journal of the American Heart Association · 2025Article
- Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population.Scientific reports · 2025Article
- Time-frequency machine learning transfer function for central pressure waveforms.European heart journal open · 2025Article
- A spectral machine learning approach to derive central aortic pressure waveforms from a brachial cuff.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Metabolic clues to aging: exploring the role of circulating metabolites in frailty, sarcopenia and vascular aging related traits and diseases.Frontiers in genetics · 2024Article
- Application of three-dimensional printing in the planning and execution of aortic aneurysm repair.Frontiers in cardiovascular medicine · 2024Review
- Tracking De-Stiffening Interventions of the Aorta: Can Wearables and Artificial Intelligence-Derived Vascular Age Biomarkers Help?Pulse (Basel, Switzerland)Article
Corrections and comments
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Authors and funding
12 authors at 8 institutions in 2 countries.
Funding
Abstract
backgroundAortic stiffness, assessed as carotid-femoral pulse wave velocity, provides a measure of vascular age and risk for adverse cardiovascular disease outcomes, but it is difficult to measure. The shape of arterial pressure waveforms conveys information regarding aortic stiffness; however, the best methods to extract and interpret waveform features remain controversial.
methodsWe trained a convolutional neural network with fixed-scale (time and amplitude) brachial, radial, and carotid tonometry waveforms as input and negative inverse carotid-femoral pulse wave velocity as label. Models were trained with data from 2 community-based Icelandic samples (N=10 452 participants with 31 126 waveforms) and validated in the community-based Framingham Heart Study (N=7208 participants, 21 624 waveforms). Linear regression rescaled predicted negative inverse carotid-femoral pulse wave velocity to equivalent artificial intelligence vascular age (AI-VA).
resultsThe AI-VascularAge model predicted negative inverse carotid-femoral pulse wave velocity with R
conclusionsOur results demonstrate that convolutional neural network-derived AI-VA is a powerful indicator of vascular health and cardiovascular disease risk in a broad community-based sample.
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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.