Safety and Feasibility of a Machine-Learning Bolus Priming Added to Existing Control Algorithm
Safety and Feasibility of a Machine-Learning Bolus Priming Added to Existing Control Algorithm
A randomized crossover trial assessing glycemic control using Reinforcement Learning trained Bolus Priming System (BPS_RL) added to the the Automated Insulin Delivery as Adaptive NETwork (AIDANET algorithm) compared to the original AIDANET algorithm.
After receiving training on the study equipment, participants will use the AIDANET system at home for 7 days/6 nights to establish a baseline and initialize the control algorithm. Participants will then be studied at a hotel session for 3 days/2 nights. Participants will transition to home use of AIDANET+ BPS_RL for 7 days/6 nights.
Inclusion Criteria:
Exclusion Criteria: