| To develop a convenient and low-cost gait tracking system based on deep learning technology for detecting gait details in experimental mice, and to preliminarily test its application in wild-type mice and
various central nervous system disease mouse models. Methods: A simple gait corridor was built, and mice were
allowed to walk freely inside the corridor for 4 minutes while their walking videos were recorded from the ventral
side. From the free movement videos of the mice, 120 frames were extracted and analyzed using DeepLabCut to
label 36 body parts for neural network training. The system and network were applied to analyze the gait of
wild-type mice at ages 1, 3, 6, and 18 months, APP/PS1 mice (6 months old, Alzheimer’s disease model), social
isolation (SI) mice (3 months old, anxiety and depression model), bilateral carotid artery stenosis (BCAS) mice (3
months old, chronic cerebral ischemia model), and sepsis-associated encephalopathy (SAE) mice at postoperative
days 1, 3, and 7 (2 months old). Results: DeepLabCut demonstrated high accuracy in all animal video tracking.
Three-month-old wild-type mice had the fastest movement speed and increased stride length compared to other
age groups. APP/PS1 mice showed significantly higher movement speed than age-matched controls, accompanied
by increased stride length and decreased standing time. SI mice exhibited shortened stride length, reduced toe
spread and toe angle of the left front paw, indicating foot posture changes. BCAS mice showed no significant
change in stride length but had significantly increased hind limb toe spread and decreased toe angle. SAE mice
showed reduced movement speed with shortened stride length and extended standing time on postoperative days 1
and 3. By day 7 post-operation, SAE mice had lower movement speed than control mice but without significant
difference, and had smaller hind limb toe spread and toe angle compared to the control group. Conclusion: This
study established a convenient, low-cost gait analysis device based on deep learning, requiring minimal effort to label body parts of interest, making it more cost-effective than previous gait analysis methods. Using this device, we
described the gait characteristics of wild-type mice across different age groups and demonstrated that mice models of Alzheimer’s disease,
anxiety and depression, chronic cerebral ischemia, and sepsis-associated encephalopathy exhibit gait deficits. |