Research on Dietary Intake Monitoring and Nutrition Assessment System for Dialysis Patients Combined with Computer Vision Technology

This study proposes a dietary intake monitoring and nutrition assessment system for dialysis patients based on computer vision technology, aiming to achieve automatic monitoring of dietary intake and accurate nutritional analysis of dialysis patients. This study introduces a deep learning convolutional neural network (CNN) for image feature extraction and uses transfer learning to enhance the model's generalization ability on various food data sets. In the simulation stage, the system is tested on the dietary images of actual dialysis patients. The results show that the system's accuracy in food recognition reaches 92.5%, and the error in nutrient estimation is controlled within 5%. The experimental data fully proves the effectiveness and reliability of the system, which can provide a scientific basis for the dietary management of dialysis patients. At the same time, this system has the advantages of high efficiency and low cost and is suitable for broad application in clinical environments. This study provides a new method for personalized nutritional assessment of dialysis patients and provides a theoretical basis and technical support for developing health management systems based on computer vision.