title: Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study.
abstract: BACKGROUND
  Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference.
  
  
  OBJECTIVE
  The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires.
  
  
  METHODS
  The study was conducted at the Bern University Hospital, "Inselspital" (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital's restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user's experience with GoCARB.
  
  
  RESULTS
  The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use.
  
  
  CONCLUSIONS
  This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.
authors.affiliation: ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
authors.forename: Daniel
authors.identifier: http://orcid.org/0000-0001-5353-9673
authors.initials: D
authors.lastname: Rhyner
authors.forename: Hannah
authors.identifier: http://orcid.org/0000-0002-7282-9403
authors.initials: H
authors.lastname: Loher
authors.forename: Joachim
authors.identifier: http://orcid.org/0000-0003-0814-8475
authors.initials: J
authors.lastname: Dehais
authors.forename: Marios
authors.identifier: http://orcid.org/0000-0003-3549-1922
authors.initials: M
authors.lastname: Anthimopoulos
authors.forename: Sergey
authors.identifier: http://orcid.org/0000-0003-0073-3450
authors.initials: S
authors.lastname: Shevchik
authors.forename: Ransford Henry
authors.identifier: http://orcid.org/0000-0003-0673-4008
authors.initials: RH
authors.lastname: Botwey
authors.forename: David
authors.identifier: http://orcid.org/0000-0003-4303-8634
authors.initials: D
authors.lastname: Duke
authors.forename: Christoph
authors.identifier: http://orcid.org/0000-0003-1691-6059
authors.initials: C
authors.lastname: Stettler
authors.forename: Peter
authors.identifier: http://orcid.org/0000-0002-1190-9880
authors.initials: P
authors.lastname: Diem
authors.forename: Stavroula
authors.identifier: http://orcid.org/0000-0002-6355-9982
authors.initials: S
authors.lastname: Mougiakakou
mesh_terms: D000328:Adult; D040421:Cell Phone; D016208:Databases, Factual; D003922:Diabetes Mellitus, Type 1 / Q000378:metabolism*; D015930:Diet Records; D004040:Dietary Carbohydrates; D004435:Eating; D006801:Humans; D062407:Meals; D057566:Self Report; D013557:Switzerland; D017216:Telemedicine / Q000379:methods*
chemical_list: D004040:Dietary Carbohydrates
keywords: carbohydrate counting; computer vision systems; diabetes mellitus, type 1; food recognition; food volume estimation; meal assessment; mobile phone