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Analgesia and Pain Management

Analgesia and Pain Management

Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...

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Related Experiment Video

Updated: May 26, 2026

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

Quantitative orbital tightening for pain assessment using machine learning with DeepLabCut.

Saurav Gupta¹, Akihiro Yamada¹, Jennifer Ling¹

¹Department of Anesthesiology and Perioperative Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, United States.

Neurobiology of Pain (Cambridge, Mass.)

|September 17, 2024

Summary

This study introduces a quantitative grimace scale (qGS) using machine learning to objectively measure pain in mice. The new method quantifies orbital tightening, offering a more consistent and reliable approach for preclinical pain research.

Keywords:

Artificial intelligence DeepLabCut Grimace scale Machine learning Orbital tightening Pain

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Last Updated: May 26, 2026

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Area of Science:

Neuroscience
Animal Models
Pain Research

Background:

Pain assessment in animal models is crucial for developing new pain medicines.
The traditional grimace scale (GS) for pain assessment is subjective and not quantitative.
Facial expressions, like orbital tightening (OT), are key indicators of pain in animals.

Purpose of the Study:

To develop a quantitative grimace scale (qGS) for objective pain assessment in mice.
To utilize machine learning (DeepLabCut) for precise measurement of orbital tightening.
To validate the qGS across different pain models in mice.

Main Methods:

DeepLabCut was used to annotate eyelid margins and canthus in animal video images.
Eyelid distance and palpebral fissure width were quantified to measure orbital tightening.
The qGS was validated in mice experiencing pain from capsaicin, laser stimulation, and oxaliplatin treatment.

Main Results:

The quantitative grimace scale (qGS) successfully measured orbital tightening.
Significant shortening of eyelid distance and palpebral fissure width was observed in pained mice.
The qGS demonstrated reliability across various pain induction methods.

Conclusions:

A novel, quantitative grimace scale (qGS) for pain assessment in mice has been established using DeepLabCut.
This method provides an objective and reliable tool for measuring orbital tightening in preclinical pain studies.
The qGS enhances the accuracy and consistency of pain assessment in animal models.