[智能无创妊娠血糖监测仪原型的临床验证]。

Heqing Lu, Lirui Cheng, Qiaoling Du, Xiaofeng Zhang, Zhenzhen Sun, Zhigang Zhu
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引用次数: 0

摘要

本研究旨在通过临床验证,评估智能无创血糖监测仪原型在孕期的有效性。该监测仪利用近红外光谱仪结合人工智能大数据分析光电容积脉搏波数据,实现对孕期妇女血糖的无创监测。研究团队研发的监测仪采用传感芯片,有效克服了无创血糖监测中信号微弱、个体差异大等问题。通过智能无创孕期血糖监测仪配套的手机 APP(应用程序)可视化检测结果,增强了用户体验。临床验证表明,20 至 30 岁孕妇的无创监测数据与传统血糖测量方法的数据相比有显著差异,而与传统血糖测量方法的数据相比无显著差异(P<0.05)。
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[Clinical Validation of a Prototype Smart Non-Invasive Pregnancy Glucose Monitor].

The aim of this study is to evaluate the effectiveness of a smart non-invasive blood glucose monitor prototype during pregnancy through clinical validation. The monitor utilizes near-infrared spectroscopy combined with AI big data analysis of photoelectric volumetric pulse wave data to achieve non-invasive monitoring of blood glucose in women during pregnancy. The research team developed a monitor that employs a sensing chip, effectively overcoming the problems of weak signals and individual differences in non-invasive blood glucose monitoring. The user experience is enhanced by visualizing the test results on the accompanying cell phone APP (application) of the smart non-invasive pregnancy blood glucose monitor. Clinical validation revealed that the non-invasive monitoring data for pregnant women aged 20~30 years significantly differed from those obtained via traditional blood glucose measurement methods, whereas no significant difference ( P<0.05) was observed for pregnant women aged 31~42 years. The study concluded that further calibration of the monitor and an expansion of the sample size are necessary to enhance consistency with invasive glucose monitoring results.

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来源期刊
中国医疗器械杂志
中国医疗器械杂志 Medicine-Medicine (all)
CiteScore
0.40
自引率
0.00%
发文量
8086
期刊介绍:
期刊最新文献
[12-Lead Holter Integrated with Sleep Monitoring Module]. [Bowel Sounds Detection Method Based on ResNet-BiLSTM and Attention Mechanism]. [Clinical Application of Equivalent Uniform Dose in Intensity-Modulated Rotational Radiotherapy Based on Eclipse TPS]. [Clinical Validation of a Prototype Smart Non-Invasive Pregnancy Glucose Monitor]. [Clinical Validation Study of Deep Learning-Generated Magnetic Resonance Images].
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