@Article{info:doi/10.2196/48690,
author="Ryu, Jiwon
and Kim, Sejoong
and Lim, Yejee
and Ohn, Jung Hun
and Kim, Sun-wook
and Cho, Jae Ho
and Park, Hee Sun
and Lee, Jongchan
and Kim, Eun Sun
and Kim, Nak-Hyun
and Song, Ji Eun
and Kim, Su Hwan
and Suh, Eui-Chang
and Mukhtorov, Doniyorjon
and Park, Jung Hyun
and Kim, Sung Kweon
and Kim, Hye Won",
title="Sodium Intake Estimation in Hospital Patients Using AI-Based Imaging: Prospective Pilot Study",
journal="JMIR Form Res",
year="2024",
month="Feb",
day="16",
volume="8",
pages="e48690",
keywords="artificial intelligence; AI; image-to-text; smart nutrition; eHealth; urine; validation; AI image; food AI; hospital; sodium intake; pilot study; imaging; diet; diet management; sex; age",
abstract="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. ",
issn="2561-326X",
doi="10.2196/48690",
url="https://formative.jmir.org/2024/1/e48690",
url="https://doi.org/10.2196/48690",
url="http://www.ncbi.nlm.nih.gov/pubmed/38363594"
}