Automatic learning algorithm for the MD-logic artificial pancreas system.

BACKGROUND: Applying real-time learning into an artificial pancreas system could effectively track the unpredictable behavior of glucose-insulin dynamics and adjust insulin treatment accordingly. We describe a novel learning algorithm and its performance when integrated into the MD-Logic Artificial Pancreas (MDLAP) system developed by the Diabetes Technology Center, Schneider Children's Medical Center of Israel, Petah Tikva, Israel. METHODS: The algorithm was designed to establish an initial patient profile using open-loop data (Initial Learning Algorithm component) and then make periodic adjustments during closed-loop operation (Runtime Learning Algorithm component). The MDLAP system, integrated with the learning algorithm, was tested in seven different experiments using the University of Virginia/Padova simulator, comprising adults, adolescents, and children. The experiments included simulations using the open-loop and closed-loop control strategy under nominal and varying insulin sensitivity conditions. The learning algorithm was automatically activated at the end of the open-loop segment and after every day of the closed-loop operation. Metabolic control parameters achieved at selected time points were compared. RESULTS: The percentage of time glucose levels were maintained within 70-180 mg/dL for children and adolescents significantly improved when open-loop was compared with day 6 of closed-loop control (P<0.0001) and remained unaltered for the adult group (P=0.11) during nominal conditions. In varying insulin sensitivity conditions, the percentage of time glucose levels were below 70 mg/dL was significantly reduced by approximately sevenfold (P<0.001). These observations were correlated with significant reduction in the Low Blood Glucose Index (P<0.001). CONCLUSIONS: The new algorithm was effective in characterizing the patient profiles from open-loop data and in adjusting treatment to provide better glycemic control during closed-loop control in both conditions. These findings warrant corroboratory clinical trials.

MD-logic artificial pancreas system: a pilot study in adults with type 1 diabetes.

OBJECTIVE: Current state-of-the-art artificial pancreas systems are either based on traditional linear control theory or rely on mathematical models of glucose-insulin dynamics. Blood glucose control using these methods is limited due to the complexity of the biological system. The aim of this study was to describe the principles and clinical performance of the novel MD-Logic Artificial Pancreas (MDLAP) System. RESEARCH DESIGN AND METHODS: The MDLAP applies fuzzy logic theory to imitate lines of reasoning of diabetes caregivers. It uses a combination of control-to-range and control-to-target strategies to automatically regulate individual glucose levels. Feasibility clinical studies were conducted in seven adults with type 1 diabetes (aged 19-30 years, mean diabetes duration 10 +/- 4 years, mean A1C 6.6 +/- 0.7%). All underwent 14 full, closed-loop control sessions of 8 h (fasting and meal challenge conditions) and 24 h. RESULTS: The mean peak postprandial (overall sessions) glucose level was 224 +/- 22 mg/dl. Postprandial glucose levels returned to <180 mg/dl within 2.6 +/- 0.6 h and remained stable in the normal range for at least 1 h. During 24-h closed-loop control, 73% of the sensor values ranged between 70 and 180 mg/dl, 27% were >180 mg/dl, and none were <70 mg/dl. There were no events of symptomatic hypoglycemia during any of the trials. CONCLUSIONS: The MDLAP system is a promising tool for individualized glucose control in patients with type 1 diabetes. It is designed to minimize high glucose peaks while preventing hypoglycemia. Further studies are planned in the broad population under daily-life conditions.