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

Formative Evaluation of Digital Health Interventions (2491) Mobile Health (mhealth) (2982) New Methods (963) Instruments and Questionnaires for Nutrition and Food Intake (169) mHealth for Wellness, Behavior Change and Prevention (2922)

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 Asbroeck1 Author Orcid Image ;   Christophe Matthys1 Author Orcid Image
Article Authors Cited by (23) Tweetations (8) 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
  11. Lyu W, Seok N, Chen X, Xu R. Using Crowdsourced Food Image Data for Assessing Restaurant Nutrition Environment: A Validation Study. Nutrients 2023;15(19):4287 View
  12. Lozano C, Canty E, Saha S, Broyles S, Beyl R, Apolzan J, Martin C. Validity of an Artificial Intelligence-Based Application to Identify Foods and Estimate Energy Intake Among Adults: A Pilot Study. Current Developments in Nutrition 2023;7(11):102009 View
  13. Zhang S, Callaghan V, Che Y. Image-based methods for dietary assessment: a survey. Journal of Food Measurement and Characterization 2024;18(1):727 View
  14. El Sherbini A, Rosenson R, Al Rifai M, Virk H, Wang Z, Virani S, Glicksberg B, Lavie C, Krittanawong C. Artificial intelligence in preventive cardiology. Progress in Cardiovascular Diseases 2024 View
  15. Morales R, Martinez-Arroyo A, Aguilar E. Robust Deep Neural Network for Learning in Noisy Multi-Label Food Images. Sensors 2024;24(7):2034 View
  16. Theodore Armand T, Nfor K, Kim J, Kim H. Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review. Nutrients 2024;16(7):1073 View
  17. Mauldin K, Pignotti G, Gieng J. Measures of nutrition status and health for weight‐inclusive patient care: A narrative review. Nutrition in Clinical Practice 2024;39(4):751 View
  18. Baumgartner M, Kuhn C, Nakas C, Herzig D, Bally L. Carbohydrate Estimation Accuracy of Two Commercially Available Smartphone Applications vs Estimation by Individuals With Type 1 Diabetes: A Comparative Study. Journal of Diabetes Science and Technology 2024 View
  19. Li X, Yin A, Choi H, Chan V, Allman-Farinelli M, Chen J. Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care. Nutrients 2024;16(15):2573 View
  20. Chung C, Chiang P, Tan C, Wu C, Schmidt H, Kotarski A, Guise D, Wong A. Opportunities to design better computer vison-assisted food diaries to support individuals and experts in dietary assessment: An observation and interview study with nutrition experts. PLOS Digital Health 2024;3(11):e0000665 View

Books/Policy Documents

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