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整合人工智能与生物传感器和电压测量用于神经递质检测和定量:系统性审查

Ibrahim Moubarak Nchouwat Ndumgouo¹, Mohammad Zahir Uddin Chowdhury¹, Silvana Andreescu²

¹Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13699, USA.

|November 26, 2025

概括

人工智能 (AI) 增强了复杂液体中的神经递质 (NT) 检测,改善了神经退行性疾病的诊断. 人工智能方法克服了生物传感器的局限性,实现实时监控和个性化治疗.

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
人工智能的人工智能

背景情况:

神经退行性疾病的准确诊断依赖于复杂生物流体中神经递质 (NT) 动态的实时监测.
目前的生物传感器缺乏灵敏度和选择性,阻碍了可靠的NT检测和量化.
挑战包括信号卷积,电极污染和NT间交叉声,限制诊断准确度.

研究的目的:

审查和综合人工智能 (AI) 应用的研究,用于自动化神经递质检测和量化.
评估机器学习 (ML),模式识别 (PR) 和深度学习 (DL),以改善复杂生物流体中的NT估计.
探索AI在克服神经退行性疾病诊断传统生物传感器局限性的潜力.

主要方法:

对33项同行评审研究的系统审查,这些研究将人工智能纳入神经递质估计.
分析通常研究的NT,检测方法和数据采集技术.
用于信号处理和NT数据解释的AI算法的分类.

主要成果:

基于人工智能的方法在解复杂,多重复合的NT信号方面显示出显著的潜力.
人工智能可以实现更准确的实时NT估计,克服传统生物传感器的局限性.
经过审查的AI方法按在NT信号分析中的应用和性能进行分类.

结论:

人工智能增强的NT监测是推动神经退行性疾病诊断和治疗的有希望的途径.
人工智能集成为更有效和个性化的治疗提供了潜力,包括闭环深度大脑刺激 (CLDBS).
需要进一步的研究来解决诸如传感器稳定性和NT交互复杂性的挑战.

关键词:

人工智能的人工智能是人工智能.生物传感器生物传感器深度学习是一种深度学习.机器学习是机器学习.这些神经递质是神经递质.模式识别模式识别电压测量是一种电压测量.

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