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

Formative Evaluation of Digital Health Interventions (2410) Usability of Apps and User Perceptions of mHealth (974) Innovations and Technology for Healthy Eating Education (414) Instruments and Questionnaires for Nutrition and Food Intake (166) mHealth for Wellness, Behavior Change and Prevention (2871) Food Sciences and Nutrition Engineering (54)

Published on in Vol 6, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31875, first published .
Nutrient and Food Group Prediction as Orchestrated by an Automated Image Recognition System in a Smartphone App (CALO mama): Validation Study

Nutrient and Food Group Prediction as Orchestrated by an Automated Image Recognition System in a Smartphone App (CALO mama): Validation Study

Nutrient and Food Group Prediction as Orchestrated by an Automated Image Recognition System in a Smartphone App (CALO mama): Validation Study

Authors of this article:

Yuki Sasaki1 Author Orcid Image ;   Koryu Sato1, 2 Author Orcid Image ;   Satomi Kobayashi1 Author Orcid Image ;   Keiko Asakura3 Author Orcid Image
Article Authors Cited by (10) Tweetations (10) Metrics

Journals

  1. Vasiloglou M, Marcano I, Lizama S, Papathanail I, Spanakis E, Mougiakakou S. Multimedia Data-Based Mobile Applications for Dietary Assessment. Journal of Diabetes Science and Technology 2023;17(4):1056 View
  2. Santo K. Can Digital Health Solutions Fill in the Gap for Effective Guideline Implementation in Cardiovascular Disease Prevention: Hope or Hype?. Current Atherosclerosis Reports 2022;24(9):747 View
  3. Nakata Y, Sasai H, Gosho M, Kobayashi H, Shi Y, Ohigashi T, Mizuno S, Murayama C, Kobayashi S, Sasaki Y. A Smartphone Healthcare Application, CALO mama Plus, to Promote Weight Loss: A Randomized Controlled Trial. Nutrients 2022;14(21):4608 View
  4. Hibino Y, Matsumoto S, Nagase H, Nakamura T, Kato Y, Isomura T, Hori M. Exploring Changes in Attitudes, Behaviors, and Self-Measured Health Data Through Lifestyle Modification Support by Community Pharmacists: Suito-Ogaki Selfcare (SOS) Trial. Integrated Pharmacy Research and Practice 2023;Volume 12:87 View
  5. Shonkoff E, Cara K, Pei X, Chung M, Kamath S, Panetta K, Hennessy E. AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review. Annals of Medicine 2023;55(2) View
  6. Pala D, Petrini G, Bosoni P, Larizza C, Quaglini S, Lanzola G. Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities. International Journal of Medical Informatics 2024;184:105351 View
  7. 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
  8. 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
  9. Imamura T, Narang N, Kinugawa K. Validation of artificial intelligence-based application to estimate nutrients in daily meals. Journal of Cardiology 2024 View