TY  - JOUR
AU  - Ryu, Jiwon
AU  - Kim, Sejoong
AU  - Lim, Yejee
AU  - Ohn, Jung Hun
AU  - Kim, Sun-wook
AU  - Cho, Jae Ho
AU  - Park, Hee Sun
AU  - Lee, Jongchan
AU  - Kim, Eun Sun
AU  - Kim, Nak-Hyun
AU  - Song, Ji Eun
AU  - Kim, Su Hwan
AU  - Suh, Eui-Chang
AU  - Mukhtorov, Doniyorjon
AU  - Park, Jung Hyun
AU  - Kim, Sung Kweon
AU  - Kim, Hye Won
PY  - 2024
DA  - 2024/2/16
TI  - Sodium Intake Estimation in Hospital Patients Using AI-Based Imaging: Prospective Pilot Study
JO  - JMIR Form Res
SP  - e48690
VL  - 8
KW  - artificial intelligence
KW  - AI
KW  - image-to-text
KW  - smart nutrition
KW  - eHealth
KW  - urine
KW  - validation
KW  - AI image
KW  - food AI
KW  - hospital
KW  - sodium intake
KW  - pilot study
KW  - imaging
KW  - diet
KW  - diet management
KW  - sex
KW  - age
AB  - 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. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2024/1/e48690
UR  - https://doi.org/10.2196/48690
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38363594
DO  - 10.2196/48690
ID  - info:doi/10.2196/48690
ER  -