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基于超声波数据的基于神经网络的动脉直径估计

Zhuangzhuang Yu^1,2, Manolis Sifalakis¹, Borbála Hunyadi²

¹Department of Signal Processing & Modelling, imec The Netherlands / Holst Centre, Eindhoven, The Netherlands.

PLOS digital health

|December 2, 2024

概括

机器学习模型通过超声波准确地跟踪动脉直径,有助于预防心血管疾病. 这种自动化方法最大限度地减少了错误,并且适用于便携式监控.

科学领域:

生物医学工程生物医学工程
医疗成像医学成像
机器学习机器学习

背景情况:

心血管疾病是导致死亡的主要原因.
通过超声波早期发现动脉异常对于预防至关重要.
自动带直径波形提取对于血液动力学分析至关重要,但由于生理变异性而具有挑战性.

研究的目的:

开发数据驱动的机器学习 (ML) 模型,从超声波数据中自动提取动脉直径.
在嵌入式系统中部署最小化计算复杂性.
在不需要临床医生的注释或手工制作的启发式信息的情况下,跟踪动脉直径.

主要方法:

使用两个卷积神经网络 (NN) 模型和平滑波器开发了一个ML管道.
第一个NN检测感兴趣的区域 (ROI),第二个NN追踪动脉直径.
超声波信号以500Hz的频率获取,并使用基于数字信号处理 (DSP) 的方法作为参考来训练ML管道.

主要成果:

ML管道与DSP参考波形实现了近乎完美的时间对齐 (皮尔森相关系数r = 0.87).
动脉直径预测的平均绝对偏差为0.077毫米 (1%的误差).
基于NN的方法证明了对漂移和工件的稳定性.

结论:

拟议的ML管道提供了一种全自动,准确和计算效率高的方法来跟踪动脉直径.
这种方法消除了对专家干预和手工微调的需求,与当前的临床实践和传统的DSP方法不同.
ML管道在小数据集上的可训练性和适合A模式超声波的适用性使其成为小型化和在线临床/健行监测的前景.

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