The demand for improved efficiency and accuracy in vaccine safety assessments
is increasing. Here, we explore the application of computer vision technologies
to automate the monitoring of experimental mice for potential side effects
after vaccine administration. Traditional observation methods are
labor-intensive and lack the capability for continuous monitoring. By deploying
a computer vision system, our research aims to improve the efficiency and
accuracy of vaccine safety assessments. The methodology involves training
machine learning models on annotated video data of mice behaviors pre- and
post-vaccination. Preliminary results indicate that computer vision effectively
identify subtle changes, signaling possible side effects. Therefore, our
approach has the potential to significantly enhance the monitoring process in
vaccine trials in animals, providing a practical solution to the limitations of
human observation.

应用计算机视觉技术自动监测实验小鼠在接种疫苗后的潜在副作用，通过训练机器学习模型对小鼠行为进行分析，初步结果表明计算机视觉能够有效识别细微变化，提高疫苗安全评估过程的效率和准确性，并解决人类观察的局限性。