  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.
