Immobility Recognition System in Tail Suspension Test Using Single Camera and Deep Learning

The tail suspension test (TST) is a widely used mouse behavioral test to evaluate the efficacy of antidepressant drugs. While the measurement of immobility, a key metric in TST, is often manually scored by a human investigator, an automated video analysis system provides more consistent and objective scoring. However, the proper validation and optimization of the automated analysis is crucial to ensure the reliability and validity of the TST results. In this study, a deep learning analysis successfully achieved accurate immobility recording using a single domestic camera observation. The system employs two deep learning models, allowing for the evaluation of temporal movement and static mouse postures. This system demonstrates immobility recognition with exceptional precision, as evidenced by a correlation coefficient (r) of 0.990 with manual annotations. The newly developed system employing deep learning models can be applied to other behavioral tests, providing an unbiased approach and contributing to advancements in various neurological research.