Search

There is a dire need for new non-opioid analgesics. However, efforts to develop new pain medicines have thus far met with limited success. This failure is partly due to an overreliance on evoked pain measures in preclinical models. Most preclinical models do not measure spontaneous pain—the main symptom of chronic pain in humans. Here we show our progress towards developing and validating a fully-automated and user-friendly spontaneous pain measurement tool by training a machine learning model to detect and quantify facial grimacing in mice. The original Mouse Grimace Scale (MGS) was developed to train researchers to detect pain in freely moving mice by quantifying characteristic facial expressions, or "grimacing". To increase the rate of adoption for this spontaneous pain measure, we recently adapted and trained a general object classification convolutional neural network (Google's Inception v1.0TM) to detect and classify the presence or absence of grimace in still images of freely-moving white mice. We call this recently published model the automated Mouse Grimace Scale, aMGS. Here we show our continuing efforts to further improve our model using new convolutional neural architecture to detect grimacing in mice of various coat colors and across different laboratory settings. Unlike our first aMGS program, our newest model can track mouse faces of various colors using continuous video input, rather than relying on still images to determine pain status. We also show the results of our latest validation experiments, including grimace assessment in mice following laparotomy, a common post-surgical pain assay. The rapidity and utility of our proposed automated pain classifier has the potential to greatly increase the adoption of the mouse grimace as a reproducible and clinically-relevant pain measure, and allow researchers to objectively and rapidly assess tonic and chronic pain states in a variety of mouse models using a measure of spontaneous pain. [ABSTRACT FROM AUTHOR]