1823-PUB: Expanded Dataset Improves Accuracy of Wrist-Worn Noninvasive BGM Using AI

Background and Aims: To determine accuracy, safety and specificity of a novel non-invasive wrist-worn continuous BGM device which analyses resonance shifts in the microwave spectrum using AI. We present results from an ongoing study in patients with T1D and T2D from an expanded dataset (cohorts 1-3)...

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Bibliographic Details
Published in:	Diabetes (New York, N.Y.) Vol. 72; no. Supplement_1; p. 1
Main Authors:	CHAUDHRY, MOHAMED S., QURESHI, MUHAMMAD R.A., BAIN, STEPHEN C., LUZIO, STEPHEN D., HANDY, CONSUELO M., LOVE, BRAD, SILVA, NUNO M.M., FERREIRA, LUIS M., WAREHAM, KATHIE, DUNSEATH, GARETH J., RYAN, JULIA, BARLOW, LUCY, CARRILLO MASSO, ISAMAR C., CRANE, JOEL H.
Format:	Journal Article
Language:	English
Published:	New York American Diabetes Association 23-06-2023
Subjects:	Accuracy Datasets Neural networks Wrist
Online Access:	Get full text
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Summary:	Background and Aims: To determine accuracy, safety and specificity of a novel non-invasive wrist-worn continuous BGM device which analyses resonance shifts in the microwave spectrum using AI. We present results from an ongoing study in patients with T1D and T2D from an expanded dataset (cohorts 1-3). Methods: In this open, pilot, adaptive design study, subjects (N=5/cohort) attended 4 test occasions (n=2/session), each ≤7 days apart. Devices automatically collected data every 60 secs for 500 msec over 3 hours/session, with plasma glucose measured every 5 mins. A global AI model was evaluated by MARD using venous blood glucose. Interim results from every 5 completed patients informed the next device iteration with 10 iterations possible across the study. Results: Data from each cohort was used to train a neural net algorithm to predict a new trial. Each cohort improved overall MARD prediction accuracy. All analyses followed a leave-one-trial-out methodology where the data for the omitted trial was predicted for each analysis cycle. Using the first cohort, the MARD was 21%, adding the second reduced MARD to 15% and with all 3 cohorts the predictive MARD was 13%. Improvement in the SEG plots showed data falling into low risk SEG categories. Conclusions: We have shown that by giving the neural network an expanded dataset, the MARD decreased to nearer commercially available minimally-invasive BGMs by 38% from a predictive MARD of 21% to 13%.
ISSN:	0012-1797 1939-327X
DOI:	10.2337/db23-1823-PUB