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

Formative Evaluation of Digital Health Interventions (3227) Usability of Apps and User Perceptions of mHealth (1188) Innovations and Technology for Healthy Eating Education (487) Instruments and Questionnaires for Nutrition and Food Intake (211) mHealth for Wellness, Behavior Change and Prevention (3311) Food Sciences and Nutrition Engineering (58)

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 (18) 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 2025;19(6):1570 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 2025;85(5):424 View
  10. Tsujisawa T, Kondo Y, Hosokawa R, Masaki C. Required knowledge of nutritional status assessment for dental professionals and effect of prosthodontic treatments. The Journal of the Kyushu Dental Society 2024;78(4):RV00006 View
  11. Eguchi K, Kubota T, Koyanagi T, Muto M. Real-World Data on Alcohol Consumption Behavior Among Smartphone Health Care App Users in Japan: Retrospective Study. Online Journal of Public Health Informatics 2025;17:e57084 View
  12. Kyan A, Sato K, Kondo N. Increased vegetable consumption in Japan using an incentivized health communication campaign with a quiz. Journal of Nutritional Science 2025;14 View
  13. Hayashi C, Tanemura N, Taniguchi M, Okano T, Toyoda H, Mashino S, Ogata S. A randomized controlled trial of mobile intervention using health support bubbles to prevent social frailty. npj Digital Medicine 2025;8(1) View
  14. Onoue T, Nishida K, Nakata Y, Hayashi F, Marutani M, Sakane N, Moriguchi J, Muto S, Kato K, Masuda I, Okamura T, Matsuzaki K, Kawamura T, Tsushita K. Effectiveness of recommendations in promoting the use of mobile health applications in health guidance: a randomized controlled trial. Journal of Occupational Health 2025;67(1) View
  15. Sato K, Inoue K, Yamaguchi T, Yagi M, Ebisawa M, Mizuno S, Yanagisawa T, Akiyama N, Node K, Kondo N, Furukawa T. Sodium Reduction Program Incorporating Genetic Profile and an AI-Based App. JAMA Network Open 2025;8(10):e2537540 View
  16. Zou M, Deng R, Qiu J, Mao K, Shang J, Zhu S, Cai L, Wang Y, Gong K, Zhou J. Dietary patterns, polygenic risk, and kidney cancer incidence: a UK Biobank cohort study. Journal of Translational Medicine 2025;23(1) View
  17. Choochaiwattana W, Jaruariyanon P, Jitpranee A, Deecharoen R, Kaewpradup T, Chusak C, Adisakwattana S. AI powered dietary proportion assessment for improving accuracy and practicality of the balanced meal plate model. Scientific Reports 2025 View

Conference Proceedings

  1. Blajovan B, Chirilă O, Stănescu D, Marcu M. 2023 27th International Conference on System Theory, Control and Computing (ICSTCC). FoodMedicine - Android-Based Food Recognition App For Guiding Patients With Nutritional Diseases View