Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors

Abstract

The automation of behavioral tracking and analysis in preclinical research can serve to advance the rate of research outcomes, increase experimental scalability, and challenge the scientific reproducibility crisis. Recent advances in the efficiency, accuracy, and accessibility of deep learning (DL) and machine learning (ML) frameworks are enabling this automation. As the ongoing opioid epidemic continues to worsen alongside increasing rates of chronic pain, there are ever-growing needs to understand opioid use disorders (OUDs) and identify non-opioid therapeutic options for pain. In this review, we examine how these related needs can be advanced by the development and validation of DL and ML resources for automated pain and withdrawal behavioral tracking. We aim to emphasize the utility of these tools for automated behavioral analysis, and we argue that currently developed models should be deployed to address novel questions in the fields of pain and OUD research.

Keywords: automated behavioral analysis; deep learning; machine learning; markerless tracking; opioid use disorder (OUD); opioid withdrawal; pain; pose estimation.

FIGURE 1

Overview of an example workflow for deep learning (DL)-assisted automated pain/withdrawal behavioral analysis. Animal behavior is first recorded on video, including both neurotypical behavior and potential pain/withdrawal behavior. Distinct frames are then sampled from the video pool, and the points of interest in the frames are manually annotated. The labeled frames are then used to train an encoder-decoder convolutional neural network (CNN) with tools such as DeepLabCut or SLEAP. Once the model is trained and achieves a desired level of accuracy, the full videos are fed into the model to generate pose-estimation data for all mice. Finally, behavioral information is extracted from the estimated pose data and quantified ahead of statistical comparisons. This extracted behavioral information can then be fed into field-standard global scoring algorithms and models, thus allowing for comparison of pain/withdrawal behavior between control and experimental groups of mice. Importantly, there are many easily accessible and open-source tools for each step of the example analysis pipeline, many of which have been tested and validated in the context of translational pain and opioid abuse research. Figure created with BioRender.com.