  Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring
(CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose
concentrations. The proposed adaptive basal-bolus algorithm (ABBA) supports
inputs from either SMBG or CGM devices to provide personalised suggestions for
the daily basal rate and prandial insulin doses on the basis of the patients'
glucose level on the previous day. The ABBA is based on reinforcement learning
(RL), a type of artificial intelligence, and was validated in silico with an
FDA-accepted population of 100 adults under different realistic scenarios
lasting three simulated months. The scenarios involve three main meals and one
bedtime snack per day, along with different variabilities and uncertainties for
insulin sensitivity, mealtime, carbohydrate amount, and glucose measurement
time. The results indicate that the proposed approach achieves comparable
performance with CGM or SMBG as input signals, without influencing the total
daily insulin dose. The results are a promising indication that AI algorithmic
approaches can provide personalised adaptive insulin optimisation and achieve
glucose control - independently of the type of glucose monitoring technology.
