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新生儿护理的高效边缘人工智能:在龙边缘设备上实施HIE分级

Leah Twomey, Sergi Gomez-Quintana, Andriy Temko

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    一个自动化系统使用EEG音频分析准确地对低氧缺血性脑病 (HIE) 进行评分. 这种人工智能工具为新生儿重症监护提供了快速,基于移动的决策支持,改善了HIE诊断.

    科学领域:

    • 神经科学是一个神经科学.
    • 人工智能的人工智能
    • 医疗技术 医疗技术 医学技术

    背景情况:

    • 缺氧缺血性脑病变 (HIE) 需要在六小时内及时诊断才能有效治疗.
    • 目前的HIE诊断方法复杂,耗时,需要专门的专业知识.
    • 准确的HIE严重程度分级对于最佳的临床管理至关重要.

    研究的目的:

    • 开发和实施一个自动化系统来对低氧缺血性脑病变 (HIE) 的严重程度进行分级.
    • 为临床医生提供可访问和安全的实时HIE诊断和决策支持工具.
    • 提高临床环境中HIE评估的效率和准确性.

    主要方法:

    • 脑电图信号被转换为音频领域.
    • 用2D卷积神经网络 (CNN) 来分类谱图表示,使用8位整数定量化.
    • 该系统在高通RB3 Gen 2边缘设备上实现,用于实时分析.

    主要成果:

    • 自动化系统在区分轻度和中度EEG异常方面取得了很高的准确性.
    • 该系统表现出强大的性能,适合新生儿重症监护机构.
    • 实现了62毫秒的快速推断时间,用于实际临床应用.

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    结论:

    • 开发的自动化HIE评分系统为临床医生提供了准确和及时的决策支持.
    • 在边缘设备上的实现使实时分析和移动可访问性成为可能.
    • 这种人工智能驱动的方法推进了HIE诊断,改善了新生儿重症监护病房的患者护理.