At a glance
ClinicalIndex Comparison RecordStandardized by ClinicalIndex from the ClinicalTrials.gov record · verify against the source.
Safety and Feasibility of a Machine-Learning Bolus Priming Added to Existing Control Algorithm
In Brief
A clinical study evaluating Automated Insulin Delivery Adaptive NETwork (AIDANET) and AIDANET+ BPS_RL→AIDANET for Type 1 Diabetes. Completed, enrolled 19 participants across 1 site.
Detailed Summary
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.
Study Details
Timeline
Interventions
Group A participants will use the AIDANET system at home for 7 days/6 nights. They will continue use of AIDANET system for 18 hours during the hotel session and then use AIDANET+BPS\_RL for 18 hours during the hotel session.
Group B participant will use the AIDANET+BPS\_RL system for 18 hours during the hotel session and will then use AIDANET system for 18 hours during the hotel session. They will continue to use AIDANET+BPS\_RL system at home for 7 days/6 night and then use the AIDANET system at home for 7 days/6 nights.