ArticlePublic health nutrition2020
Validating an automated image identification process of a passive image-assisted dietary assessment method: proof of concept.
Article in Public health nutrition, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Relative Validation of an Artificial Intelligence-Enhanced, Image-Assisted Mobile App for Dietary Assessment in Adults: Randomized Crossover Study.Journal of medical Internet research · 2022Trial
- Calorie-Counting Apps for Monitoring and Managing Calorie Intake in Adults Living With Weight-Related Chronic Diseases: Decade-Long Scoping Review (2013-2024).JMIR mHealth and uHealth · 2026Article
- Breakdancing Movement Based on Image Recognition Promotes Preschool Children's Executive Function and Intervention Plan.Computational and mathematical methods in medicine · 2022Article
- Emotional Intervention and Education System Construction for Rural Children Based on Semantic Analysis.Occupational therapy international · 2022Article
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Authors and funding
4 authors.
Funding
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Abstract
objectiveTo validate an automated food image identification system, DietCam, which has not been validated, in identifying foods with different shapes and complexities from passively taken digital images.
designParticipants wore Sony SmartEyeglass that automatically took three images per second, while two meals containing four foods, representing regular- (i.e., cookies) and irregular-shaped (i.e., chips) foods and single (i.e., grapes) and complex (i.e., chicken and rice) foods, were consumed. Non-blurry images from the meals' first 5 min were coded by human raters and compared with DietCam results. Comparisons produced four outcomes: true positive (rater/DietCam reports yes for food), false positive (rater reports no food; DietCam reports food), true negative (rater/DietCam reports no food) or false negative (rater reports food; DietCam reports no food).
settingLaboratory meal.
participantsThirty men and women (25·1 ± 6·6 years, 22·7 ± 1·6 kg/m2, 46·7 % White).
resultsIdentification accuracy was 81·2 and 79·7 % in meals A and B, respectively (food and non-food images) and 78·7 and 77·5 % in meals A and B, respectively (food images only). For food images only, no effect of food shape or complexity was found. When different types of images, such as 100 % food in the image and on the plate, <100 % food in the image and on the plate and food not on the plate, were analysed separately, images with food on the plate had a slightly higher accuracy.
conclusionsDietCam shows promise in automated food image identification, and DietCam is most accurate when images show food on the plate.
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