Usability evaluation of selected picture archiving and communication systems at the national level: Analysis of users' viewpoints

Analysis of the Relationship between National Image Building and International Communication Ability from the Perspective of Ecology under the Media Environment

With the gradual expansion of China's political, economic, and military strength, the rapid rise of China has become a globally recognized fact. At present, although China's international image as the world's second largest economy, military power, and political power has been increasingly accepted by the global community, China's image construction, as an important part of national soft power, is still facing many problems. This chapter focuses on defining the basic concepts of international publicity, China's soft power, and China's image construction in the context of new media integration, analyzes the international development trend of international publicity and the important functions and limitations of foreign publicity in establishing China's image construction, and analyzes the ecological environment of media integration development. Considering that there are great unfairness in the environment and the conflict of communication values is not conducive to the establishment of China's urban image, the article focuses on how to use global publicity values to establish China's national image and puts forward corresponding countermeasures.

Intelligent Algorithm-Based Picture Archiving and Communication System of MRI Images and Radiology Information System-Based Medical Informatization

Objective

The study aimed to explore the application value of picture archiving and communication system (PCAS) of MRI images based on radial basis function (RBF) neural network algorithm combined with the radiology information system (RIS).

Methods

551 patients who required MRI examination in a hospital from May 2016 to May 2021 were selected as research subjects. Patients were divided into two groups according to their own wishes. Those who were willing to use the RBF neural network algorithm-based PCAS of MRI images combined with RIS were set as the combined group, involving a total of 278 cases; those who were unwilling were set as the regular group, involving a total of 273 cases. The RBF neural network algorithm-based PCAS of MRI images combined with RIS was trained and tested for classification performance and then used for comparison analysis.

Result

The actual output (0.031259–0.038515) of all test samples was almost the same as the target output (0.000000) (P > 0.05). In the first 50,000 learnings, the iteration error of the RBF neural network dropped rapidly and finally stabilized at 0.038. The classification accuracy of the RBF neural network algorithm-based PCAS of MRI images combined with RIS for the head was 94.28%, that of abdomen was 97.22%, and it was 93.10% for knee joint, showing no statistically significant differences (P > 0.05), and the total classification accuracy was as high as 95%. The time spent in the examination in the combined group was about 2 hours, and that in the regular group was about 4 hours (P > 0.05). The satisfaction of the combined group (96.76%) was significantly higher than that of the control group (46.89%) (P > 0.05).

Conclusion

The RBF neural network has good classification performance for MRI images. To incorporate intelligent algorithms into the medical information system can optimize the system. RBF has good application prospects in the medical information system, and it is worthy of continuous exploration.