  Malnutrition is a multidomain problem affecting 54% of older adults in
long-term care (LTC). Monitoring nutritional intake in LTC is laborious and
subjective, limiting clinical inference capabilities. Recent advances in
automatic image-based food estimation have not yet been evaluated in LTC
settings. Here, we describe a fully automatic imaging system for quantifying
food intake. We propose a novel deep convolutional encoder-decoder food network
with depth-refinement (EDFN-D) using an RGB-D camera for quantifying a plate's
remaining food volume relative to reference portions in whole and modified
texture foods. We trained and validated the network on the pre-labelled
UNIMIB2016 food dataset and tested on our two novel LTC-inspired plate datasets
(689 plate images, 36 unique foods). EDFN-D performed comparably to
depth-refined graph cut on IOU (0.879 vs. 0.887), with intake errors well below
typical 50% (mean percent intake error: -4.2%). We identify how standard
segmentation metrics are insufficient due to visual-volume discordance, and
include volume disparity analysis to facilitate system trust. This system
provides improved transparency, approximates human assessors with enhanced
objectivity, accuracy, and precision while avoiding hefty semi-automatic method
time requirements. This may help address short-comings currently limiting
utility of automated early malnutrition detection in resource-constrained LTC
and hospital settings.
