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

Formative Evaluation of Digital Health Interventions (3272) Mobile Health (mhealth) (3491) New Methods (1140) Instruments and Questionnaires for Nutrition and Food Intake (215) mHealth for Wellness, Behavior Change and Prevention (3339)

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 (33) Tweetations (8) Metrics

Journals

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  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
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  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 2025;19(6):1570 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
  21. Connelly J, Swingler K, Rodriguez-Sanchez N, Whittaker A. Identifying Food Preferences and Malnutrition in Older Adults in Care Homes: Co-Design Study of a Digital Nutrition Assessment Tool. JMIR Aging 2025;8:e64661 View
  22. Sahoo P, Chiu S, Lin Y, Chen C, Irianti D, Chen H, Sarkar M, Liu Y. Automatic Image Recognition Meal Reporting Among Young Adults: Randomized Controlled Trial. JMIR mHealth and uHealth 2025;13:e60070 View
  23. Bayram H, Ozturkcan A. Applications of generative and predictive AI in nutrition and dietetics: a narrative review. Informatics for Health and Social Care 2025;50(3-4):133 View
  24. Yan R, Luo H, Lu J, Liu D, Posluszny H, Dhaliwal M, MacLeod J, Qin Y, Yang C, Hartman T, Hu X. DietAI24 as a framework for comprehensive nutrition estimation using multimodal large language models. Communications Medicine 2025;5(1) View

Books/Policy Documents

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Conference Proceedings

  1. Karannagoda R, Perera Y, Weiman D, Fernando S. 2023 8th International Conference on Information Technology Research (ICITR). Occlusion Resilient Similar-Colored Separable Food Item Instance Segmentation View
  2. Morales R, Muñoz B, Martínez-Arroyo A, Aguilar E. 2024 14th International Conference on Pattern Recognition Systems (ICPRS). SAI-ChileanDiet: A multi-label food dataset with self-acquired images of the Chilean diet View
  3. Maharana B, Goyal M, Abidi A. 2025 3rd International Conference on Disruptive Technologies (ICDT). Bridging the Gap: From Food Recognition to Accurate Weight Estimation View
  4. Bhavsar J, Khara S, Dave J. 2025 International Conference on Emerging Trends in Industry 4.0 Technologies (ICETI4T). Artificial Intelligence in Nutrition: A Comprehensive Review of Applications and Challenges View
  5. Singh Bisht G, Madan P, Vishnoi A, Narooka P, Singh Bisht K, Gupta S. 2025 International Conference on Sustainability, Innovation & Technology (ICSIT). Measurement of Sugar Intake in Food Using Image Recognition and Volume Estimation View
  6. Bhetuwal S, Koivunen L, Koskimäki S, Khalil R, Läahde H, Houttu V, Laitinen K, Mäkilä T. 2025 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT). Automated Image Recognition System for Determining Energy Composition of Meals by AI-Powered Detection and Identification of Food Items – A Study Utilizing Flavoria Flex View