Quantitative orbital tightening for pain assessment using machine learning with DeepLabCut

Abstract

Pain assessment in animal models is essential for understanding mechanisms underlying pathological pain and developing effective pain medicine. The grimace scale (GS), facial expression features in pain such as orbital tightening (OT), is a valuable measure for assessing pain in animal models. However, the classical grimace scale for pain assessment is labor-intensive, subject to subjectivity and inconsistency, and is not a quantitative measure. In the present study, we utilized machine learning with DeepLabCut to annotate the superior and inferior eyelid margins and the medial and lateral canthus of the eyes in animals’ video images. Based on the annotation, we quantified the eyelid distance and palpebral fissure width of the animals’ eyes so that the degree of OT in animals with pain could be measured and described quantitatively. We established criteria for the inclusion and exclusion of the annotated images for quantifying OT, and validated our quantitative grimace scale (qGS) in the mice with pain caused by capsaicin injections in the orofacial or hindpaw regions, the Nav1.8-ChR2 mice following orofacial noxious stimulation with laser light, and the oxaliplatin-treated mice following tactile stimulation with a von Frey filament. We showed that both the eyelid distance and the palpebral fissure width were shortened significantly in the animals in pain compared to the control animals without nociceptive stimulation. Collectively, the present study has established a quantitative orbital tightening for pain assessment in mice using DeepLabCut, providing a new tool for pain assessment in preclinical studies with mice.