Miniaturized Neural Networks for Deploying Fully Closed Loop Insulin Delivery Systems: A Pilot Study Featuring Flexible Meal Announcement Options

Abstract

Background:

Automated insulin delivery (AID) has revolutionized glucose management. Next-generation AID systems focus on reducing user input, particularly for mealtime dosing, aiming for fully closed loop (FCL) control. Our goal was to assess the safety and feasibility of the next iteration of FCL control, using a miniature neural network to enable implementation within existing hardware capabilities.

Methods:

In a randomized crossover trial, six adults with type 1 diabetes completed seven days of usual care and seven days using AIDANET in free-living conditions. AIDANET is designed to enable FCL control, but carbohydrate counting and a novel easy-bolus strategy were enabled for one day each to test the system in hybrid closed loop modalities.

Results:

The mean glucose during usual care was 168 ± 24.3 mg/dL, compared to 161.3 ± 16.7 mg/dL using the AIDANET system. Time-in-range (TIR) 70 to 180 mg/dL was 63.3% ± 14.9% in usual care compared to 66.4% ± 8.3% using AIDANET, while time-below-range (TBR) 70 mg/dL remained within acceptable margins (0.9 ± 1 vs 1.6 ± 1.8). There were no serious adverse events during the study. The hybrid bolusing options provided safe glycemic control, with carbohydrate counting achieving 57.1% TIR with 0.6% TBR, and Easy Bolus achieving 70.5% TIR with 1.5% TBR.

Conclusion:

This pilot-feasibility study demonstrates that the AIDANET system provides safe glycemic control. The small sample size (n = 6) limits overall generalizability, and further larger, statistically powered trials to validate these results are warranted.