Background: Measurement of sodium intake in hospitalized patients is critical for their care. In this study, artificial intelligence (AI)-based imaging was performed to determine sodium intake in these patients.
Objective: The applicability of a diet management system was evaluated using AI-based imaging to assess the sodium content of diets prescribed for hospitalized patients.
Methods: Based on the information on the already investigated nutrients and quantity of food, consumed sodium was analyzed through photographs obtained before and after a meal. We used a hybrid model that first leveraged the capabilities of the You Only Look Once, version 4 (YOLOv4) architecture for the detection of food and dish areas in images. Following this initial detection, 2 distinct approaches were adopted for further classification: a custom ResNet-101 model and a hyperspectral imaging-based technique. These methodologies focused on accurate classification and estimation of the food quantity and sodium amount, respectively. The 24-hour urine sodium (UNa) value was measured as a reference for evaluating the sodium intake.
Results: Results were analyzed using complete data from 25 participants out of the total 54 enrolled individuals. The median sodium intake calculated by the AI algorithm (AI-Na) was determined to be 2022.7 mg per day/person (adjusted by administered fluids). A significant correlation was observed between AI-Na and 24-hour UNa, while there was a notable disparity between them. A regression analysis, considering patient characteristics (eg, gender, age, renal function, the use of diuretics, and administered fluids) yielded a formula accounting for the interaction between AI-Na and 24-hour UNa. Consequently, it was concluded that AI-Na holds clinical significance in estimating salt intake for hospitalized patients using images without the need for 24-hour UNa measurements. The degree of correlation between AI-Na and 24-hour UNa was found to vary depending on the use of diuretics.
Conclusions: This study highlights the potential of AI-based imaging for determining sodium intake in hospitalized patients.
Keywords: AI; AI image; age; artificial intelligence; diet; diet management; eHealth; food AI; hospital; image-to-text; imaging; pilot study; sex; smart nutrition; sodium intake; urine; validation.
©Jiwon Ryu, Sejoong Kim, Yejee Lim, Jung Hun Ohn, Sun-wook Kim, Jae Ho Cho, Hee Sun Park, Jongchan Lee, Eun Sun Kim, Nak-Hyun Kim, Ji Eun Song, Su Hwan Kim, Eui-Chang Suh, Doniyorjon Mukhtorov, Jung Hyun Park, Sung Kweon Kim, Hye Won Kim. Originally published in JMIR Formative Research (https://formative.jmir.org), 16.02.2024.