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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. You only look once (YOLO v4)-based models and convolutional neural networks, including ResNet-101, were used to classify food and dish areas as well as food quantity, respectively. The 24-h urine sodium (UNa) value was measured as a reference for evaluating the sodium intake. RESULTS Among the 54 people enrolled, 25 participants with full data were analyzed. The results revealed that the median sodium intake calculated by the AI algorithm (AI-Na) was 2022.7 mg per day/person (adjusted by administered fluids). Although the 24-h UNa revealed a significant relationship with AI-Na along with the estimated glomerular filtration rate, the AI-Na calculations and 24-h UNa measurements differed considerably. Finally, a formula was derived using regression with an interaction term considering patients’ characteristics, such as sex, age, renal function, the use of diuretics, and administered fluids; thus, AI-Na has clinical significance in the calculation of salt intake in hospitalized patients using images without measuring 24-h UNa. Furthermore, we estimated that AI-Na corresponds to the 24-h UNa, dependent on a factor of 2.355 in the diuretics group and 0.353 in the non-diuretics group, indicating that the use of diuretics affects sodium excretion. CONCLUSIONS This study highlights the potential of AI-based imaging for determining sodium intake in hospitalized patients. |
