title: Computer vision-based carbohydrate estimation for type 1 patients with diabetes using smartphones.
abstract: BACKGROUND
  Individuals with type 1 diabetes (T1D) have to count the carbohydrates (CHOs) of their meal to estimate the prandial insulin dose needed to compensate for the meal's effect on blood glucose levels. CHO counting is very challenging but also crucial, since an error of 20 grams can substantially impair postprandial control.
  
  
  METHODS
  The GoCARB system is a smartphone application designed to support T1D patients with CHO counting of nonpacked foods. In a typical scenario, the user places a reference card next to the dish and acquires 2 images with his/her smartphone. From these images, the plate is detected and the different food items on the plate are automatically segmented and recognized, while their 3D shape is reconstructed. Finally, the food volumes are calculated and the CHO content is estimated by combining the previous results and using the USDA nutritional database.
  
  
  RESULTS
  To evaluate the proposed system, a set of 24 multi-food dishes was used. For each dish, 3 pairs of images were taken and for each pair, the system was applied 4 times. The mean absolute percentage error in CHO estimation was 10 ± 12%, which led to a mean absolute error of 6 ± 8 CHO grams for normal-sized dishes.
  
  
  CONCLUSIONS
  The laboratory experiments demonstrated the feasibility of the GoCARB prototype system since the error was below the initial goal of 20 grams. However, further improvements and evaluation are needed prior launching a system able to meet the inter- and intracultural eating habits.
authors.affiliation: Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 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, Bern, Switzerland Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland.
authors.forename: Joachim
authors.initials: J
authors.lastname: Dehais
authors.affiliation: Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
authors.forename: Sergey
authors.initials: S
authors.lastname: Shevchik
authors.affiliation: Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
authors.forename: Botwey H
authors.initials: BH
authors.lastname: Ransford
authors.affiliation: Diabetes Care, Roche Diagnostics Operations Inc, Indianapolis, IN, USA.
authors.forename: David
authors.initials: D
authors.lastname: Duke
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, 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: D016208:Databases, Factual; D003922:Diabetes Mellitus, Type 1 / Q000178:diet therapy*; D003927:Diet, Diabetic / Q000379:methods*; D004040:Dietary Carbohydrates; D004435:Eating; D005247:Feeding Behavior; D006801:Humans; D021621:Imaging, Three-Dimensional; D007332:Insulin Infusion Systems; D020407:Internet; D063731:Mobile Applications; D015203:Reproducibility of Results; D000068997:Smartphone
chemical_list: D004040:Dietary Carbohydrates
keywords: carbohydrate counting; computer vision; smartphone; type 1 diabetes