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隐含的HbA1c 在90天内达到87%的准确度,在非侵入性禁食血糖测量中使用光电解剖学测量
Justin Chu1,2, Yao-Ting Chang3, Shien-Kuei Liaw1
1Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd., Taipei City 10607, Taiwan.
Bioengineering (Basel, Switzerland)
|October 28, 2023
概括
这项研究引入了一种"隐性HbA1c"方法,用于从PPG数据中预测血糖水平,比以前的方法提高了16%的准确性. 这种方法通过消除直接测量HbA1c的需要来提高可用性.
科学领域:
- 生物医学工程 生物医学工程
- 医学诊断 医学诊断 医学诊断
- 机器学习 机器学习
背景情况:
- 由于过度配合/不足配合,单独的光电显微镜 (PPG) 在预测非侵入性血糖 (NIBG) 水平方面存在局限性.
- 之前采用HbA1c的模型将NIBG预测提高了10%,但需要额外的测量,从而降低了用户友好性.
- 需要准确的NIBG预测方法,这些方法是实用的和用户友好的.
研究的目的:
- 开发和评估使用PPG数据进行NIBG预测的增强深度学习模型.
- 将明确的HbA1c测量取代由PPG和血糖水平衍生的新型"隐性HbA1c".
- 评估隐式HbA1c方法对NIBG预测的准确性和实用性.
主要方法:
- 使用1494次测量训练了一种增强的HbA1c NIBG深度学习模型.
- 显式HbA1c被隐式HbA1c取代,用预先测试的PPG和手指刺穿的血糖反向计算.
- 评估了隐式HbA1c模型在90天的间隔内表现的表现.
主要成果:
- 隐性HbA1c方法在NIBG预测中实现了87%的准确性,其余预测在克拉克错误格 (CEG) 区域A边界附近.
- 与明确的HbA1c方法相比,其准确度提高了16%.
- 非参数威尔科克森配对测试显示隐式和显式HbA1c方法之间的预测误差有显著差异 (p < 2.75 × 10-7).
结论:
- 隐式HbA1c方法通过自动纳入个人校正因子,显著提高了NIBG预测的准确性和实用性.
- 这种方法为需要明确HbA1c测量的模型提供了更为用户友好的替代方案.
- 这项研究验证了从PPG和血糖中获得的隐性HbA1c用于改进NIBG监测的潜力.
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