Leveraging Deep Learning for Approaching Automated Pre-Clinical Rodent Models

We evaluate deep learning architectures for rat pose estimation using a six-camera system, focusing on ResNet and EfficientNet across various depths and augmentation techniques. Among the configurations tested, ResNet 152 with default augmentation provided the best performance when employing a multi-perspective network approach in the controlled experimental setup. It reached a Root Mean Squared Error (RMSE) of 8.74, 8.78, and 9.72 pixels for the different angles. The utilization of data augmentation revealed that less altering yields better performance. We propose potential areas for future research, including further refinement of model configurations, more in-depth investigation of inference speeds, and the possibility of transferring network weights to study other species, such as mice. The findings underscore the potential for deep learning solutions to advance preclinical research in behavioral neuroscience. We suggest building on this research to introduce behavioral recognition based on a 3D movement reconstruction, particularly emphasizing the motoric aspects of neurodegenerative diseases. This will allow for the correlation of observable behaviors with neuronal activity, contributing to a better understanding of the brain and aiding in developing new therapeutic strategies.