Enhancing Food Intake Tracking in Long-Term Care with Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology

TL;DR

This paper introduces AFINI-T, a deep learning system that automates food imaging and nutrient intake tracking in long-term care, aiming to improve accuracy and objectivity over subjective methods.

Contribution

It presents a novel convolutional autoencoder for food classification and demonstrates its effectiveness in estimating nutrient intake accurately in LTC settings.

Findings

Food classification accuracy of 88.9%

Strong correlation (r^2 0.92-0.99) between volume-based and mass-based nutrient estimates

Potential for improved malnutrition tracking in LTC

Abstract

Half of long-term care (LTC) residents are malnourished increasing hospitalization, mortality, morbidity, with lower quality of life. Current tracking methods are subjective and time consuming. This paper presents the automated food imaging and nutrient intake tracking (AFINI-T) technology designed for LTC. We propose a novel convolutional autoencoder for food classification, trained on an augmented UNIMIB2016 dataset and tested on our simulated LTC food intake dataset (12 meal scenarios; up to 15 classes each; top-1 classification accuracy: 88.9%; mean intake error: -0.4 mL$\pm$36.7 mL). Nutrient intake estimation by volume was strongly linearly correlated with nutrient estimates from mass ($r^2$ 0.92 to 0.99) with good agreement between methods ($\sigma$= -2.7 to -0.01; zero within each of the limits of agreement). The AFINI-T approach is a deep-learning powered computational nutrient sensing system that may provide a novel means for more accurately and objectively tracking LTC resident food intake to support and prevent malnutrition tracking strategies.