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使用人工智能提高手腕佩戴的非侵入性葡萄糖监测器的准确性:试点研究
Muhammad Rafaqat Ali Qureshi1, Stephen Charles Bain2,3, Stephen Luzio3
1Afon Technology, Monmouthshire, UK.
Journal of diabetes science and technology
|May 17, 2024
概括
这项研究表明,使用微波信号和人工智能 (AI) 的新型非侵入性葡萄糖监测系统的准确性与当前方法相提并论. 这为糖尿病管理提供了更方便的选择.
科学领域:
- 生物医学工程 生物医学工程
- 医疗器械 医疗器械
- 人工智能的人工智能
背景情况:
- 目前用于糖尿病管理的葡萄糖监测方法具有侵入性,并且可能不愉快.
- 需要准确而非侵入性的葡萄糖监测解决方案.
研究的目的:
- 调查一种新的非侵入性血糖监测系统的准确性.
- 评估利用微波信号和人工智能 (AI) 的系统.
主要方法:
- 进行了一项开放标签的试点研究,与四个队列 (N=5/队列) 进行了试验.
- 一个拨号共振传感器 (DRS) 每60秒收集一次微波信号数据.
- 一个人工智能模型将信号数据转化为葡萄糖预测,与血葡萄糖测量进行验证.
主要成果:
- 该系统在所有队伍中平均实现了10.3%的MARD.
- 统计分析证实了预测的质量,监测错误网格 (SEG) 上没有高风险错误.
结论:
- 由人工智能驱动的微波生物传感器显示了准确和方便的葡萄糖监测的潜力.
- 这种非侵入性方法可以改善用户的糖尿病管理.
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