Abstract：
UNASSIGNED: Self-monitoring of glucose is important to the successful management of diabetes; however, existing monitoring methods require a degree of invasive measurement which can be unpleasant for users. This study investigates the accuracy of a noninvasive glucose monitoring system that analyses spectral variations in microwave signals.
UNASSIGNED: An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values.
UNASSIGNED: Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones.
UNASSIGNED: These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.
UNASSIGNED: An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values.
UNASSIGNED: Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones.
UNASSIGNED: These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.
摘要：
■血糖的自我监测对于糖尿病的成功管理很重要；然而，现有的监测方法需要一定程度的侵入性测量,这对于用户来说可能是不愉快的。这项研究调查了分析微波信号光谱变化的无创葡萄糖监测系统的准确性。
■开放标签，试验设计研究由4个队列进行(N=5/队列).在每个会话中,连接到手腕的拨号盘共振传感器(DRS)每60秒自动收集一次数据，利用新颖的人工智能(AI)模型将信号共振输出转换为葡萄糖预测。对于队列1至3,每5分钟测量静脉血样品中的血浆葡萄糖,对于队列4,每10分钟测量。通过计算DRS和血浆葡萄糖值之间的平均绝对相对差（MARD）来评估准确性。
■使用应用于整个四个队列数据集的随机抽样程序，在所有四个队列中获得了准确的血浆葡萄糖预测，平均MARD为10.3%。统计分析证明了这些预测的质量，监视错误网格（SEG）图表明没有数据对落入高风险区域。
■这些发现表明，可以从应用AI的多参与者试点研究中获得接近与当前商业替代品相当的准确性的MARD值。微波生物传感器和AI模型有望提高糖尿病患者血糖监测系统的准确性和便利性。
■开放标签，试验设计研究由4个队列进行(N=5/队列).在每个会话中,连接到手腕的拨号盘共振传感器(DRS)每60秒自动收集一次数据，利用新颖的人工智能(AI)模型将信号共振输出转换为葡萄糖预测。对于队列1至3,每5分钟测量静脉血样品中的血浆葡萄糖,对于队列4,每10分钟测量。通过计算DRS和血浆葡萄糖值之间的平均绝对相对差（MARD）来评估准确性。
■使用应用于整个四个队列数据集的随机抽样程序，在所有四个队列中获得了准确的血浆葡萄糖预测，平均MARD为10.3%。统计分析证明了这些预测的质量，监视错误网格（SEG）图表明没有数据对落入高风险区域。
■这些发现表明，可以从应用AI的多参与者试点研究中获得接近与当前商业替代品相当的准确性的MARD值。微波生物传感器和AI模型有望提高糖尿病患者血糖监测系统的准确性和便利性。
