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通过有机神经形态微网络预测血糖水平

Ibrahim Kurt¹, Imke Krauhausen^1,2, Simone Spolaor¹

¹Microsystems, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, 5612 AE, The Netherlands.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|April 29, 2024

概括

本研究探讨使用人工神经网络 (ANN) 进行糖尿病管理中的葡萄糖预测. 它研究将这些ANN集成到智能植入式设备的紧,低功耗有机电子产品中.

科学领域:

生物医学工程生物医学工程
材料科学材料科学材料科学
计算机科学计算机科学

背景情况:

准确的葡萄糖预测对于有效的糖尿病管理至关重要.
人工智能 (AI) 和人工神经网络 (ANN) 显示出可靠的葡萄糖水平预测的潜力.
将ANN转换为专用硬件是实现自动化胰岛素输送和改善患者生活质量的关键.

研究的目的:

研究将基于软件的神经网络集成到硬件系统中,用于葡萄糖预测.
为应对开发专业化,紧,轻量化和低功耗硬件用于体内计算的挑战.
探索在智能植入物中用于神经形态计算的有机聚合物电子的使用.

主要方法:

专注于尽量减少神经网络的要求和缩小网络架构.
研究神经网络与电化学神经形态有机设备的集成.
评估使用先进材料进行体内计算以预测葡萄糖的可行性.

主要成果:

基于有机聚合物的电子产品提供了一个有前途的解决方案,因为它们的低压操作,灵活性,伸展性和生物相容性.
该研究探讨了满足智能植入物对实时葡萄糖预测的严格要求的方法.
成功实施可能会导致植入可预测设备和完全的人工胰腺.

结论:

有机电子提供了一个可行的途径,用于在葡萄糖预测中为ANN创建紧的低功耗硬件.
这项研究为先进的可植入装置为自动化糖尿病管理铺平了道路.
开发用于体内葡萄糖计算的智能植入物是改善糖尿病患者护理的重要一步.

关键词:

葡萄糖预测的预测硬件计算硬件计算计算在体内计算计算.神经网络的神经网络的神经网络有机神经形态计算是有机的神经形态计算.可以穿戴的可穿戴设备.

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