Brain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms
A recent study explores the prediction of brain vascular age using cerebral blood flow velocity and machine learning algorithms. The research analyzes data from both healthy and diseased subjects to assess accelerated cerebrovascular aging. Findings suggest that features derived from transcranial Doppler measurements may be significant in evaluating brain health.
- ▪The study involved 168 healthy subjects and 277 subjects with various brain diseases.
- ▪Transcranial Doppler (TCD) was used to measure cerebral blood flow velocity in the major arteries of the brain.
- ▪The machine learning model predicted that healthy subjects had a cerebrovascular age 3.69 years older than their chronological age.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.16969 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.16969 (cs) [Submitted on 16 May 2026] Title:Brain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms Authors:Anni Zhao, Alex Bateh, Tyler Baldridge, Sandra Billinger, Xiao Hu View a PDF of the paper titled Brain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms, by Anni Zhao and 4 other authors View PDF HTML (experimental) Abstract:Defining vascular age in terms of physiological function has become one focal point of the extensive studies to categorize and track chronological age. Transcranial Doppler (TCD) is a method by which cerebral blood flow velocity is measured along the major arteries feeding the human brain.
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