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Malnutrition is a multidomain problem affecting 54% of older adults inlong-term care (LTC). Monitoring nutritional intake in LTC is laborious andsubjective, limiting clinical inference capabilities. Recent advances inautomatic image-based food estimation have not yet been evaluated in LTCsettings. Here, we describe a fully automatic imaging system for quantifyingfood intake. We propose a novel deep convolutional encoder-decoder food networkwith depth-refinement (EDFN-D) using an RGB-D camera for quantifying a plate'sremaining food volume relative to reference portions in whole and modifiedtexture foods. We trained and validated the network on the pre-labelledUNIMIB2016 food dataset and tested on our two novel LTC-inspired plate datasets(689 plate images, 36 unique foods). EDFN-D performed comparably todepth-refined graph cut on IOU (0.879 vs. 0.887), with intake errors well belowtypical 50% (mean percent intake error: -4.2%). We identify how standardsegmentation metrics are insufficient due to visual-volume discordance, andinclude volume disparity analysis to facilitate system trust. This systemprovides improved transparency, approximates human assessors with enhancedobjectivity, accuracy, and precision while avoiding hefty semi-automatic methodtime requirements. This may help address short-comings currently limitingutility of automated early malnutrition detection in resource-constrained LTCand hospital settings.

scientific reports

When Segmentation is Not Enough: Rectifying Visual-Volume Discordance  Through Multisensor Depth-Refined Semantic Segmentation for Food Intake  Tracking in Long-Term Care