ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland.
Geriatrische Klinik St. Gallen AG, Rorschacherstrasse 94, 9000 St. Gallen, Switzerland.
Nutrients. 2021 Dec 17;13(12):4539. doi: 10.3390/nu13124539.
Malnutrition is common, especially among older, hospitalised patients, and is associated with higher mortality, longer hospitalisation stays, infections, and loss of muscle mass. It is therefore of utmost importance to employ a proper method for dietary assessment that can be used for the identification and management of malnourished hospitalised patients. In this study, we propose an automated Artificial Intelligence (AI)-based system that receives input images of the meals before and after their consumption and is able to estimate the patient's energy, carbohydrate, protein, fat, and fatty acids intake. The system jointly segments the images into the different food components and plate types, estimates the volume of each component before and after consumption, and calculates the energy and macronutrient intake for every meal, based on the kitchen's menu database. Data acquired from an acute geriatric hospital as well as from our previous study were used for the fine-tuning and evaluation of the system. The results from both our system and the hospital's standard procedure were compared to the estimations of experts. Agreement was better with the system, suggesting that it has the potential to replace standard clinical procedures with a positive impact on time spent directly with the patients.
营养不良很常见，尤其是在年老、住院的患者中，并且与更高的死亡率、更长的住院时间、感染和肌肉量损失有关。因此，采用适当的饮食评估方法来识别和管理营养不良的住院患者至关重要。在这项研究中，我们提出了一种基于人工智能 (AI) 的自动化系统，该系统接收患者进食前后的图像输入，并能够估计患者的能量、碳水化合物、蛋白质、脂肪和脂肪酸摄入量。该系统联合对图像进行分段，将不同的食物成分和餐盘类型分开，估计进食前后每个成分的体积，并根据厨房的菜单数据库计算每餐的能量和宏量营养素摄入量。从一家急性老年病医院和我们之前的研究中获取的数据用于系统的微调与评估。我们的系统和医院标准程序的结果与专家的估计进行了比较。我们的系统与专家的估计结果更为一致，这表明它有可能用积极的影响取代标准的临床程序，直接与患者进行接触。
