%0 Journal Article
%@ 2561-326X
%I JMIR Publications
%V 8
%N 
%P e48690
%T Sodium Intake Estimation in Hospital Patients Using AI-Based Imaging: Prospective Pilot Study
%A Ryu,Jiwon
%A Kim,Sejoong
%A Lim,Yejee
%A Ohn,Jung Hun
%A Kim,Sun-wook
%A Cho,Jae Ho
%A Park,Hee Sun
%A Lee,Jongchan
%A Kim,Eun Sun
%A Kim,Nak-Hyun
%A Song,Ji Eun
%A Kim,Su Hwan
%A Suh,Eui-Chang
%A Mukhtorov,Doniyorjon
%A Park,Jung Hyun
%A Kim,Sung Kweon
%A Kim,Hye Won
%+ Hospital Medicine Center, Seoul National University Bundang Hospital, Gumi-ro 173 Beon-gil 82, Bundang-gu, Seongnam-si, 13620, Republic of Korea, 82 7877638, kimhwhw@gmail.com
%K artificial intelligence
%K AI
%K image-to-text
%K smart nutrition
%K eHealth
%K urine
%K validation
%K AI image
%K food AI
%K hospital
%K sodium intake
%K pilot study
%K imaging
%K diet
%K diet management
%K sex
%K age
%D 2024
%7 16.2.2024
%9 Original Paper
%J JMIR Form Res
%G English
%X 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. 
%M 38363594
%R 10.2196/48690
%U https://formative.jmir.org/2024/1/e48690
%U https://doi.org/10.2196/48690
%U http://www.ncbi.nlm.nih.gov/pubmed/38363594