用机器学习分析脑血管血流数据，自动识别动脉瘤和动静脉畸形。
Machine learning for cerebral blood vessels' malformations
- 基于血流速度与压力的线性振荡模型，支持毫秒级在线参数重建。
- 通过逻辑回归分类实现73%准确率，可自动区分脑血管病理类型。
- 模型可解释性强，适合临床风险评估与术后预后判断。
脑动脉瘤和动静脉畸形是威胁生命的脑部血流动力学疾病。尽管手术干预常为必需，但术中及术后风险显著，管理极具挑战。临床手术或高分辨率无创成像中常规监测的脑血流参数，可应用于机器学习辅助的风险评估与治疗预后。为此，我们针对神经外科手术获取的临床数据，构建了血流速度与压力的线性振荡模型。利用稀疏非线性动力学识别方法（SINDy），可在毫秒级内从短时序数据中在线重构模型参数。所识别的参数值通过逻辑回归实现病理自动分类，准确率达73%。结果表明该模型在诊断与预后应用中具有潜力，提供了一种稳健且可解释的脑血管状态评估框架。
原文摘要 · Abstract (English)
Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain. While surgical intervention is often essential to prevent fatal outcomes, it carries significant risks both during the procedure and in the postoperative period, making the management of these conditions highly challenging. Parameters of cerebral blood flow, routinely monitored during medical interventions or with modern noninvasive high-resolution imaging methods, could potentially be utilized in machine learning-assisted protocols for risk assessment and therapeutic prognosis. To this end, we developed a linear oscillatory model of blood velocity and pressure for clinical data acquired from neurosurgical operations. Using the method of Sparse Identification of Nonlinear Dynamics (SINDy), the parameters of our model can be reconstructed online within milliseconds from a short time series of the hemodynamic variables. The identified parameter values enable automated classification of the blood-flow pathologies by means of logistic regression, achieving an accuracy of 73 \%}. Our results demonstrate the potential of this model for both diagnostic and prognostic applications, providing a robust and interpretable framework for assessing cerebral blood vessel conditions.
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