# Development of a deep neural network to measure spontaneous pain from mouse facial expressions

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $364,998

## Abstract

PROJECT SUMMARY
Opioid analgesics are commonly used to treat pain but have serious side effects, including addiction,
dependence, and death from overdose. While there is a significant need for new non-addictive analgesics,
efforts to develop new pain medicines have met with limited success. In part, this failure is due to an
overreliance on evoked pain measures in preclinical models. Indeed, most preclinical models do not measure
spontaneous pain—the main symptom of chronic pain in humans. To increase translational relevance, the
Mouse Grimace Scale (MGS) was developed to quantify characteristic facial expressions associated with
spontaneous pain. The MGS is reproducible across labs and was used to evaluate the efficacy of analgesics.
However, the MGS has not been widely adopted due to its high resource demands and low throughput. To
overcome this limitation, we adapted a machine learning model to classify the presence or absence of pain
from mouse facial expressions. We called this model the automated Mouse Grimace Scale (aMGS). After
training, this model identified mice in pain with 94% accuracy, comparable to a highly-trained human. However,
our original “aMGS 1.0” is limited in several respects. It is only accurate at detecting facial grimacing in white-
coated mice, and produces a binary assessment (“pain” vs. “no pain”) instead of a graded score. Moreover,
aMGS 1.0 cannot dynamically determine pain status from full-motion videos. Additionally, we relied on an older
piece of software that does not consistently extract high-quality images of the mouse face. The aMGS 1.0 also
has difficulty distinguishing between images of sleeping and grimacing mice. Finally, aMGS 1.0 suffers from a
“black box” problem inherent to most machine learning algorithms, in that we do not know what facial details it
uses to produce a pain assessment. Here we propose to overcome all of these limitations by developing
a more sophisticated version of our automated pain classifier (aMGS 2.0). To achieve this goal we will: 1)
Develop and validate a new open-source platform to classify (frame-by-frame) spontaneous pain intensity from
mouse facial expressions, using albino (white) mice and motion information. 2) Enhance the generality of
aMGS 2.0 for use with black mice. And, 3) Develop a user-friendly web-based platform that operates on
computer-based and mobile devices. We will validate the utility of aMGS with three pain assays that produce
grimaces in rodents—inflammatory pain, post-surgical (laparotomy) pain, and neuropathic pain. To increase
rigor and reproducibility, two pain assays will be performed and scored with aMGS 2.0 in an independent lab.
Numerous investigators in the pain field have expressed interest in using our proposed model. The platform
will include a cloud-based data repository and analytic tools to facilitate curation of public data, continuous
improvement of the model over time, and integration of new analytic tools. One analytic tool that we plan...

## Key facts

- **NIH application ID:** 9866055
- **Project number:** 1R01NS114259-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Mark J. Zylka
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $364,998
- **Award type:** 1
- **Project period:** 2020-02-15 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9866055

## Citation

> US National Institutes of Health, RePORTER application 9866055, Development of a deep neural network to measure spontaneous pain from mouse facial expressions (1R01NS114259-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9866055. Licensed CC0.

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