基于机器学习的脉搏波分析用于威利斯圈拓扑分类:利用 30,618 名虚拟受试者进行的硅学研究

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Ahmet Sen , Miquel Aguirre , Peter H Charlton , Laurent Navarro , Stéphane Avril , Jordi Alastruey
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引用次数: 0

摘要

背景和目的 威利斯圈(CoW)的拓扑结构在脑循环中至关重要,对患者的管理有重大影响。不完整的CoW结构会增加中风风险和中风后的损害。目前使用计算机断层扫描和磁共振扫描进行检测的方法往往具有侵入性、耗时且成本高昂。本研究调查了使用机器学习(ML)通过动脉血流速度脉搏波(PW)对CoW拓扑结构进行分类的方法,脉搏波可通过多普勒超声进行无创测量。方法建立了一个来自30618名虚拟受试者(年龄在25至75岁之间)的具有完整和不完整CoW拓扑结构的硅学脉搏波数据库,并根据体内数据进行了验证。使用 45 种颈动脉、椎动脉和肱动脉 PW 组合对七种 ML 架构进行了训练和测试,并使用不同程度的人工噪音来模拟真实世界的测量误差。结果卷积神经网络使用一个椎动脉和一个颈总动脉速度 PW 组合进行 CoW 拓扑分类的准确率最高(98%),且无噪声。在噪声信号比为 20% 的情况下,多层感知器模型的预测率最高(79%)。所有 ML 模型在缺乏后交通动脉的拓扑结构中表现最佳。平均收缩速度和峰值收缩速度被认为是影响 ANN 预测的关键特征。数据集、后处理工具和 ML 代码可免费提供,以支持进一步的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning-based pulse wave analysis for classification of circle of Willis topology: An in silico study with 30,618 virtual subjects

Background and Objective

The topology of the circle of Willis (CoW) is crucial in cerebral circulation and significantly impacts patient management. Incomplete CoW structures increase stroke risk and post-stroke damage. Current detection methods using computed tomography and magnetic resonance scans are often invasive, time-consuming, and costly. This study investigated the use of machine learning (ML) to classify CoW topology through arterial blood flow velocity pulse waves (PWs), which can be noninvasively measured with Doppler ultrasound.

Methods

A database of in silico PWs from 30,618 virtual subjects, aged 25 to 75 years, with complete and incomplete CoW topologies was created and validated against in vivo data. Seven ML architectures were trained and tested using 45 combinations of carotid, vertebral and brachial artery PWs, with varying levels of artificial noise to mimic real-world measurement errors. SHapley Additive exPlanations (SHAP) were used to interpret the predictions made by the artificial neural network (ANN) models.

Results

A convolutional neural network achieved the highest accuracy (98%) for CoW topology classification using a combination of one vertebral and one common carotid velocity PW without noise. Under a 20% noise-to-signal ratio, a multi-layer perceptron model had the highest prediction rate (79%). All ML models performed best for topologies lacking posterior communication arteries. Mean and peak systolic velocities were identified as key features influencing ANN predictions.

Conclusions

ML-based PW analysis shows significant potential for efficient, noninvasive CoW topology detection via Doppler ultrasound. The dataset, post-processing tools, and ML code, are freely available to support further research.
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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