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使用机器学习检测动脉样硬化脏动脉狭窄症从光谱多普勒波形的试点研究

Haseeb Mukhtar^1,2, Seyed Moein Rassoulinejad-Mousavi^1,3,4,5, Shahriar Faghani^1,3

¹Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.

Kidney international reports

|April 30, 2025

概括

机器学习准确地检测动脉样硬化性动脉狭窄症 (ARAS),使用来自双重超声 (DUS) 的光谱多普勒波形. 这种人工智能方法实现了95%的准确性,为诊断ARAS提供了一个有前途的工具.

科学领域:

医疗成像医学成像
人工智能的人工智能
心血管疾病心血管疾病

背景情况:

动脉硬性动脉狭窄 (ARAS) 是二次高血压和病的重要原因.
准确的ARAS诊断对于及时干预和管理至关重要.
双重超声波 (DUS) 光谱多普勒波形提供有价值的血液动力学信息,但需要专家的解释.

研究的目的:

调查机器学习 (ML) 在检测ARAS方面的有效性.
为了利用来自脏DUS的光谱多普勒波形来基于ML的ARAS分类.
开发和验证使用DUS数据识别ARAS的ML模型.

主要方法:

已确认单边ARAS患者接受支架安置的患者的回顾性分析.
使用WebPlot Digitizer从脏DUS中提取和处理光谱多普勒波形.
在标记速度信号上训练一维卷积神经网络 (CNN) 模型,用于ARAS检测.

主要成果:

ML模型实现了0.95的整体精度和0.94.9的精度.
接收器运行特征曲线 (AUC) 下的面积为0.97,表明高诊断性能.
该模型成功地将光谱多普勒信号分类为ARAS或正常.

结论:

机器学习可以有效地检测ARAS使用光谱多普勒波形在脏DUS.
开发的ML模型证明了ARAS诊断的高准确性和可靠性.
这种人工智能驱动的方法有可能改善ARAS检测和患者管理.

关键词:

在多普勒测试中,多普勒动脉样硬化动脉样硬化双重扫描是一个复杂的扫描.这种高血压,高血压.机器学习是机器学习.动脉狭窄症动脉狭窄症

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