Evidence map›Paper›PMID 39286765›Full record

ArticleNeurobiology of pain (Cambridge, Mass.)

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

Saurav Gupta, Akihiro Yamada, Jennifer Ling, Jianguo G Gu

Abstract read
In one paragraph

Article in Neurobiology of pain (Cambridge, Mass.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Saurav GuptaDepartment of Anesthesiology and Perioperative Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, United States.
Akihiro YamadaDepartment of Anesthesiology and Perioperative Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, United States.
Jennifer LingDepartment of Anesthesiology and Perioperative Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, United States.
Jianguo G GuDepartment of Anesthesiology and Perioperative Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, United States.

Funding

Mechanisms of Nociception in the Trigeminal SystemR01DE018661 · NIDCR · UNIVERSITY OF FLORIDA · PI JIANGUO GU · 2008 to 2026
$6.9M
Cellular and Ion Channel Mechanisms Underlying the Sense of Light Touch in MammalsR01DE023090 · NIDCR · UNIVERSITY OF CINCINNATI · PI JIANGUO GU · 2013 to 2026
$6.1M
Ion channels and their functions at the node of Ranvier of mammalian somatosensory afferent fibersR01NS109059 · NINDS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI GU, JIANGUO · 2019 to 2023
$2.2M
NIDCR NIH HHS R01 DE018661NIDCR NIH HHS R01 DE023090NINDS NIH HHS R01 NS109059
6 · The paper itself

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.

Indexed as

Artificial intelligenceDeepLabCutGrimace scaleMachine learningOrbital tighteningPain

Identifiers

PMID39286765
PMCPMC11404079

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.