Published on in Vol 8, No 3 (2020): March

Preprints (earlier versions) of this paper are available at, first published .
Volumetric Food Quantification Using Computer Vision on a Depth-Sensing Smartphone: Preclinical Study

Volumetric Food Quantification Using Computer Vision on a Depth-Sensing Smartphone: Preclinical Study

Volumetric Food Quantification Using Computer Vision on a Depth-Sensing Smartphone: Preclinical Study


  1. Steenkamp D, Eby E, Gulati N, Liao B. Adherence and Persistence to Insulin Therapy in People with Diabetes: Impact of Connected Insulin Pen Delivery Ecosystem. Journal of Diabetes Science and Technology 2022;16(4):995 View
  2. Pfisterer K, Amelard R, Chung A, Syrnyk B, MacLean A, Keller H, Wong A. Automated food intake tracking requires depth-refined semantic segmentation to rectify visual-volume discordance in long-term care homes. Scientific Reports 2022;12(1) View
  3. Schönenberger K, Cossu L, Prendin F, Cappon G, Wu J, Fuchs K, Mayer S, Herzig D, Facchinetti A, Bally L. Digital Solutions to Diagnose and Manage Postbariatric Hypoglycemia. Frontiers in Nutrition 2022;9 View
  4. Jia W, Ren Y, Li B, Beatrice B, Que J, Cao S, Wu Z, Mao Z, Lo B, Anderson A, Frost G, McCrory M, Sazonov E, Steiner-Asiedu M, Baranowski T, Burke L, Sun M. A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation. Sensors 2022;22(4):1493 View
  5. Barmanray R, Kyi M, Fourlanos S. Assistive technology for diabetes management: a toolkit. British Journal of Healthcare Management 2022;28(5):118 View
  6. Amugongo L, Kriebitz A, Boch A, Lütge C. Mobile Computer Vision-Based Applications for Food Recognition and Volume and Calorific Estimation: A Systematic Review. Healthcare 2022;11(1):59 View
  7. 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
  8. Moshfegh A, Rhodes D, Martin C. National Food Intake Assessment: Technologies to Advance Traditional Methods. Annual Review of Nutrition 2022;42(1):401 View
  9. Karabay A, Bolatov A, Varol H, Chan M. A Central Asian Food Dataset for Personalized Dietary Interventions. Nutrients 2023;15(7):1728 View
  10. 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
  11. Serra M, Alceste D, Hauser F, Hulshof P, Meijer H, Thalheimer A, Steinert R, Gerber P, Spector A, Gero D, Bueter M. Assessing daily energy intake in adult women: validity of a food-recognition mobile application compared to doubly labelled water. Frontiers in Nutrition 2023;10 View
  12. 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
  13. Zhang R, Ouyang D, He L, Kuang L, Bai H. Recognize after early fusion: the Chinese food recognition based on the alignment of image and ingredients. Multimedia Systems 2024;30(2) View

Books/Policy Documents

  1. Nagarajan B, Khatun R, Bolaños M, Aguilar E, Angelini L, El Kamali M, Mugellini E, Khaled O, Boqué N, Tarro L, Radeva P. Digital Health Technology for Better Aging. View
  2. Chummun P, Suddul G, Armoogum S. Proceedings of World Conference on Information Systems for Business Management. View