This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (1595) Mobile Health (mhealth) (2007) New Methods (689) Instruments and Questionnaires for Nutrition and Food Intake (141) mHealth for Wellness, Behavior Change and Prevention (2342)

Published on in Vol 4 , No 12 (2020) :December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15602, first published .
Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study

Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study

Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study

Authors of this article:

Stephanie Van Asbroeck 1 Author Orcid Image ;   Christophe Matthys 1 Author Orcid Image
Article Authors Cited by (11) Tweetations (11) Metrics

Journals

  1. Rantala E, Balatsas-Lekkas A, Sozer N, Pennanen K. Overview of objective measurement technologies for nutrition research, food-related consumer and marketing research. Trends in Food Science & Technology 2022;125:100 View
  2. Liu Y, Onthoni D, Mohapatra S, Irianti D, Sahoo P. Deep-Learning-Assisted Multi-Dish Food Recognition Application for Dietary Intake Reporting. Electronics 2022;11(10):1626 View
  3. Lara-Breitinger K, Lynch M, Kopecky S. Nutrition Intervention in Cardiac Rehabilitation. Journal of Cardiopulmonary Rehabilitation and Prevention 2021;41(6):383 View
  4. Hu H, Zhang Q, Chen Y. NIRSCam: A Mobile Near-Infrared Sensing System for Food Calorie Estimation. IEEE Internet of Things Journal 2022;9(19):18934 View
  5. Samad S, Ahmed F, Naher S, Kabir M, Das A, Amin S, Islam S. Smartphone apps for tracking food consumption and recommendations: Evaluating artificial intelligence-based functionalities, features and quality of current apps. Intelligent Systems with Applications 2022;15:200103 View
  6. Amorim D, Miranda F, Ferreira L, Abreu C. Data-Driven Carbohydrate Counting Accuracy Monitoring: A Personalized Approach. Procedia Computer Science 2022;204:900 View
  7. Moshfegh A, Rhodes D, Martin C. National Food Intake Assessment: Technologies to Advance Traditional Methods. Annual Review of Nutrition 2022;42(1):401 View
  8. Chen X, Johnson E, Kulkarni A, Ding C, Ranelli N, Chen Y, Xu R. An Exploratory Approach to Deriving Nutrition Information of Restaurant Food from Crowdsourced Food Images: Case of Hartford. Nutrients 2021;13(11):4132 View
  9. Moyen A, Rappaport A, Fleurent-Grégoire C, Tessier A, Brazeau A, Chevalier S. Relative Validation of an Artificial Intelligence–Enhanced, Image-Assisted Mobile App for Dietary Assessment in Adults: Randomized Crossover Study. Journal of Medical Internet Research 2022;24(11):e40449 View
  10. Hicks J, Boswell M, Althoff T, Crum A, Ku J, Landay J, Moya P, Murnane E, Snyder M, King A, Delp S. Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective. Annual Review of Public Health 2023;44(1):131 View

Books/Policy Documents

  1. Hamilton S, Richards T, Roe B. . View