arXiv cs.AI· Anni Zhao, Alex Bateh, Tyler Baldridge, Sandra Billinger, Xiao Hu·· 12 小时前AI 评分19
利用脑血流速度与机器学习算法预测脑血管年龄
Brain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms
AI 导读
研究用经颅多普勒（TCD）提取大脑中动脉血流速度特征，结合 MOCAIP 算法与心率变异性特征训练回归模型预测脑血管年龄。模型对 168 名健康受试者的预测值平均比实际年龄高 3.69 岁，并在 66 名急性卒中、27 名卒中后、26 名阿尔茨海默病、23 名轻度认知障碍及 135 名其他受试者中观察到不同程度的血管年龄加速。研究指出数据不平衡会影响机器学习脑龄预测模型的性能。
正文 · 原文
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. This study aims to use features extracted from TCD to estimate chronological age and assess accelerated aging in subjects with various brain diseases. We predict subjects with various brain diseases to present with accelerated cerebrovascular aging when tested on various regression models trained by healthy subjects. 168 healthy subjects and 277 diseased subjects with bilateral TCD recordings of the middle cerebral artery were analyzed using the Morphological Analysis and Clustering of Intracranial Pressure (MOCAIP) algorithm. MOCAIP-generated features and heart rate variability features were used as input features for regression models to predict the brain vascular age. 66 subjects with acute stroke, 27 subjects with post stroke, 26 subjects with Alzheimer's disease, 23 subjects with mild cognitive impairment, and 135 established subjects were tested against the machine learning model to assess for accelerated cerebrovascular age. The trained model, on average, predicted healthy subjects' cerebrovascular age to be 3.69 years above their chronological age. Subjects with different disease conditions exhibited varying levels of age acceleration. The differences in healthy and diseased subjects' performances suggest that features generated using TCD may be relevant when evaluating accelerated cerebrovascular aging. Moreover, imbalanced datasets have been observed to affect the performance of machine-learning-based brain age prediction models.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.16969 [cs.AI] |
| (or arXiv:2605.16969v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16969 arXiv-issued DOI via DataCite |
Submission history
From: Anni Zhao [view email]
[v1]
Sat, 16 May 2026 12:43:32 UTC (858 KB)
[v2]
Tue, 14 Jul 2026 00:38:03 UTC (858 KB)
[v3]
Thu, 15 Oct 2026 18:16:01 UTC (858 KB)
来源：arXiv cs.AI · arxiv.org