title: GoCARB in the Context of an Artificial Pancreas.
abstract: BACKGROUND
  In an artificial pancreas (AP), the meals are either manually announced or detected and their size estimated from the blood glucose level. Both methods have limitations, which result in suboptimal postprandial glucose control. The GoCARB system is designed to provide the carbohydrate content of meals and is presented within the AP framework.
  
  
  METHODS
  The combined use of GoCARB with a control algorithm is assessed in a series of 12 computer simulations. The simulations are defined according to the type of the control (open or closed loop), the use or not-use of GoCARB and the diabetics' skills in carbohydrate estimation.
  
  
  RESULTS
  For bad estimators without GoCARB, the percentage of the time spent in target range (70-180 mg/dl) during the postprandial period is 22.5% and 66.2% for open and closed loop, respectively. When the GoCARB is used, the corresponding percentages are 99.7% and 99.8%. In case of open loop, the time spent in severe hypoglycemic events (<50 mg/dl) is 33.6% without the GoCARB and is reduced to 0.0% when the GoCARB is used. In case of closed loop, the corresponding percentage is 1.4% without the GoCARB and is reduced to 0.0% with the GoCARB.
  
  
  CONCLUSIONS
  The use of GoCARB improves the control of postprandial response and glucose profiles especially in the case of open loop. However, the most efficient regulation is achieved by the combined use of the control algorithm and the GoCARB.
authors.affiliation: Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland.
authors.forename: Aristotelis
authors.initials: A
authors.lastname: Agianniotis
authors.affiliation: Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland.
authors.forename: Marios
authors.initials: M
authors.lastname: Anthimopoulos
authors.affiliation: Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland.
authors.forename: Elena
authors.initials: E
authors.lastname: Daskalaki
authors.affiliation: Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland.
authors.forename: Aurélie
authors.initials: A
authors.lastname: Drapela
authors.affiliation: Department of Endocrinology, Diabetes & Clinical Nutrition, Bern University Hospital "Inselspital," Bern, Switzerland.
authors.forename: Christoph
authors.initials: C
authors.lastname: Stettler
authors.affiliation: Department of Endocrinology, Diabetes & Clinical Nutrition, Bern University Hospital "Inselspital," Bern, Switzerland.
authors.forename: Peter
authors.initials: P
authors.lastname: Diem
authors.affiliation: Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland Department of Endocrinology, Diabetes & Clinical Nutrition, Bern University Hospital "Inselspital," Bern, Switzerland stavroula.mougiakakou@artorg.unibe.ch.
authors.forename: Stavroula
authors.initials: S
authors.lastname: Mougiakakou
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis; D040421:Cell Phone; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000178:diet therapy / Q000188:drug therapy*; D004040:Dietary Carbohydrates / Q000032:analysis; D006801:Humans; D006943:Hyperglycemia / Q000517:prevention & control; D007003:Hypoglycemia / Q000517:prevention & control; D007332:Insulin Infusion Systems; D019397:Pancreas, Artificial; D019518:Postprandial Period
chemical_list: D001786:Blood Glucose; D004040:Dietary Carbohydrates
keywords: artificial pancreas; carbohydrate counting; computer vision; control algorithm; type 1 diabetes