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.

In neuroscience research, in vivo imaging techniques for mice are used to observe brain activity and link it to their behavior. Brain activity can often only be associated with observed behavioral outcomes. In other words, it is difficult to speculate on unmanifested behavior due to factors such as “hesitation” in humans. When a prediction model can predict mice behavior, if brain activity is observed in a specific brain region during incorrect predictions, that would be strong evidence of unmanifest behavior. In this study, we developed a trajectory prediction model to predict the walking trajectory of mice as a prelude to the behavior prediction model. The prediction model was applied to the behavioral analysis of mice administered an anxiolytic drug (diazepam) or saline, revealing significantly different outcomes.

Injuries during the practice of sports in gymnasiums have been reported, and one of the causes of injuries is due to environmental factors as tiny wood splinters on the gymnasium floor. Although it is important to regularly inspect gymnasium floors, it is difficult for humans to inspect the entire gymnasium floor, as it is done manually and visually, and requires a lot of time and manpower. We have developed an automatic inspection system to detect tiny splinters on the gymnasium floor. The system attaches cotton to tiny splinters and detects the attached cotton by using an image processing technique. Using this system, the entire gymnasium floor can be inspected automatically by using simply creating a 2D map. After the inspection, the system can show where splinters are located on the map. In this paper, the method for detecting splinters attached to cotton using deep learning object detection-YOLO was proposed. The detection ratio of the proposed method was improved by 25.0 % compared to the conventional method of threshold color segmentation process. In an inspection of an entire gymnasium, the proposed method detected 33 markers and was able to detect splinters that could cause injury.