DeepLabCut to Automate Behavioral Analysis of Parkinsonism.

Background: Behavioral assessment of parkinsonism often relies on human rater evaluation. However, human biases and variability necessitate larger sample sizes to maintain validity, leading to extensive video analysis and limiting researchers' time. Recent artificial intelligence (AI) and machine learning (ML) advancements enable efficient data analysis, offering unbiased decision-making and consistency across scenarios, bridging inter-rater differences. While not fully automating jobs, AI/ML boosts productivity when properly trained with diverse data. This study aims to show that AI/ML can assist in the analysis of rat parkinsonian behavioral studies to reduce labor dependence while still maintaining accuracy.

Methods: DeepLabCut (DLC), an animal pose estimation software, was used to analyze motor behavior in video recordings of parkinsonian Sprague Dawley rats while they performed the stepping test ( = 24). The stepping test involves observing the animal's locomotor function and motor coordination while it is guided across a flat surface. The amount of adjusting steps was counted over the 1-meter distance. Twenty-eight videos ( = 24 + 4 training videos) were fed into DLC, which then selected 20 frames per video using a k-nearest neighbors' algorithm and subsequently labeled to train the model. This one-time training process took 3 h. The output, which has the tracked coordinates of the forepaw being tested, was fed into a script in R to plot Δ y between consecutive frames. The positive peaks were counted as one step, and large negative peaks were counted as a reset or side switch. The counts for each video were then compared with an independent manual rater.

Results: There was good absolute agreement between the two scoring methods, using the two-way random effect model, kappa = 0.9, < 0.0001. It takes 10-15 min to go through each video manually, but the DLC-assisted scoring resulted in 3-4 min per video. These results show that DLC-assisted scoring produced results that could be on par with manual scoring. In addition, this shows a feasible avenue to integrate AI/ML in parkinsonian behavioral studies to reduce the workload for analysis and eventually, fully automating such tasks.