Development and validation of point-of-care mobile solution to guide the diagnosis of malnutrition: A multicenter, prospective cohort study.

11042 Background: Malnutrition has negative effects on patients with chronic diseases, leading to reduced treatment tolerance, increased risk of clinical complications, and even death. This research was aimed to develop a point-of-care program based on facial features to screen malnourished inpatient patients. Methods: In this prospective, multicenter, cohort study, we retrieved facial photograph and malnutrition screening scales from 4 different hospitals. We utilized a variety of machine learning models to explore whether the ocular area could serve as an enhanced region for facial recognition nutrition. Last, we utilized a facial area segmentation and weighted approach to retain the information of full-face features using a BP neural network and validated using Delong-test, IDI-test, and NRI-test. Overall, 619 inpatients’ facial photographs and their corresponding nutrition screening questionnaires were used to train, validate and test the machine learning model. Results: The Pearson correlation analysis showed a significant correlation (p<0.05) between the two questionnaires in all groups. The average AUC obtained from the five-fold cross-validation set was 0.886 (CI 0.843-0.930), 0.834 (CI 0.764-0.904), and 0.927 (CI 0.899-0.955) for the Cancer Inpatient Group, Other Inpatient Group, and All Inpatient Group, respectively, with the corresponding AUC obtained from the external validation set being 0.860 (CI 0.817-0.904), 0.843 (CI 0.796-0.889), and 0.887 (CI 0.829-0.944). Conclusions: The facial photograph-based point of-care mobile solution can screen malnutrition with good accuracy, showing its potential for screening malnutrition in inpatients in the hospital in different types of diseases.