Enhancing Food Intake Tracking in Long-term Care With Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology: Validation and Feasibility Assessment.

Kaylen J Pfisterer Robert Amelard Jennifer Boger Heather H Keller Audrey Chung Alexander Wong

Published in: JMIR aging (2022)

The automated food imaging and nutrient intake tracking approach is a deep learning-powered computational nutrient sensing system that appears to be feasible (validated accuracy against gold-standard weighed food method, positive end user engagement) and may provide a novel means for more accurate and objective tracking of LTC residents' food intake to support and prevent malnutrition tracking strategies.

Keyphrases

deep learning
high resolution
machine learning
human health
artificial intelligence
convolutional neural network
physical activity
weight loss
silver nanoparticles