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Published Online: 30 May 2024

Neural-Net Artificial Pancreas: A Randomized Crossover Trial of a First-in-Class Automated Insulin Delivery Algorithm

Publication: Diabetes Technology & Therapeutics
Volume 26, Issue Number 6

Abstract

Background: Automated insulin delivery (AID) is now integral to the clinical practice of type 1 diabetes (T1D). The objective of this pilot-feasibility study was to introduce a new regulatory and clinical paradigm—a Neural-Net Artificial Pancreas (NAP)—an encoding of an AID algorithm into a neural network that approximates its action and assess NAP versus the original AID algorithm.
Methods: The University of Virginia Model-Predictive Control (UMPC) algorithm was encoded into a neural network, creating its NAP approximation. Seventeen AID users with T1D were recruited and 15 participated in two consecutive 20-h hotel sessions, receiving in random order either NAP or UMPC. Their demographic characteristics were ages 22–68 years old, duration of diabetes 7–58 years, gender 10/5 female/male, White Non-Hispanic/Black 13/2, and baseline glycated hemoglobin 5.4%–8.1%.
Results: The time-in-range (TIR) difference between NAP and UMPC, adjusted for entry glucose level, was 1 percentage point, with absolute TIR values of 86% (NAP) and 87% (UMPC). The two algorithms achieved similar times <70 mg/dL of 2.0% versus 1.8% and coefficients of variation of 29.3% (NAP) versus 29.1 (UMPC)%. Under identical inputs, the average absolute insulin-recommendation difference was 0.031 U/h. There were no serious adverse events on either controller. NAP had sixfold lower computational demands than UMPC.
Conclusion: In a randomized crossover study, a neural-network encoding of a complex model-predictive control algorithm demonstrated similar performance, at a fraction of the computational demands. Regulatory and clinical doors are therefore open for contemporary machine-learning methods to enter the AID field.
Clinical Trial Registration number: NCT05876273.

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Information & Authors

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Published In

cover image Diabetes Technology & Therapeutics
Diabetes Technology & Therapeutics
Volume 26Issue Number 6June 2024
Pages: 375 - 382
PubMed: 38277161

History

Published in print: June 2024
Published online: 30 May 2024
Published ahead of production: 26 January 2024

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    Authors

    Affiliations

    Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA.
    Alberto Castillo
    Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA.
    Elliott Pryor
    Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA.
    Laura L. Kollar
    Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA.
    Charlotte L. Barnett
    Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA.
    Mark D. DeBoer
    Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA.
    Sue A. Brown
    Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA.

    Notes

    Address correspondence to: Boris Kovatchev, PhD, Center for Diabetes Technology, University of Virginia School of Medicine, 560 Ray C Hunt Drive, Charlottesville, VA 22903, USA [email protected]

    Authors' Contributions

    B.K. co-created the NAP concept, contributed to the study design, was the sponsor of the Investigational Device Exemption by FDA, and wrote the first draft of this manuscript. A.C. and E.P. co-designed the NAP algorithm and contributed to the engineering study design, data analysis, and manuscript writing. L.K. was Project and Nursing Manager and contributed to the writing of this manuscript. C.B. was responsible for the data retrieval and contributed to the writing of this manuscript. M.D.D. and S.A.B. contributed to the study design, trial execution as the study physicians, and manuscript writing.

    Author Disclosure Statement

    B.K. declares research support from Dexcom, Novo Nordisk, and Tandem Diabetes Care, and patent royalties handled by the University of Virginia's Licensing and Ventures Group from Dexcom, Lifescan, Novo Nordisk, and Sanofi. M.D.B. has received research support to UVA from Dexcom, Tandem Diabetes Care, and Medtronic. S.A.B. has received research support to UVA from Dexcom, Insulet, Roche, Tandem Diabetes Care, and Tolerion.

    Funding Information

    National Institutes of Health/National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK) Grant RO1 DK 133148. RedCap at the University of Virginia is supported in part by the National Center for Advancing Translational Sciences of the NIH under Award # UL1TR003015.

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