Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monitor: A Pilot Study
Authors' conclusion
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.
Study context
- Comparison
- Wrist-worn noninvasive microwave dial-resonating sensor (DRS) with artificial intelligence (AI) model vs plasma glucose
- Outcome
- Accuracy: mean absolute relative difference (MARD) between DRS and plasma glucose values
Methodological detailsopen-label, four cohorts
- Open-label
- Four cohorts
Main finding
As reported in the abstract
Average mean absolute relative difference (MARD) across all four cohorts (random sampling applied to full four-cohort data set)
10.3%
Further findings
Surveillance error grid (SEG)
- No data pairs falling into the high-risk zones
Original publication
Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monitor: A Pilot Study. J Diabetes Sci Technol. Published 17 May 2024. doi:10.
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