ReviewPain reports2026
Artificial intelligence-based automated scoring of the mouse grimace scale: a systematic review.
Review in Pain reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Mouse Grimace Scale (MGS) is a widely adopted facial expression-based tool for pain assessment. However, manual scoring is labor-intensive and subject to both intra- and inter-observer variability. Artificial intelligence-based approaches have recently been developed to automate MGS analysis, yet their performance and methodological quality have not been systematically evaluated. This systematic review was undertaken to evaluate studies applying artificial intelligence-based methods for automated MGS scoring. We searched PubMed, Web of Science, Scopus, and IEEE Xplore up to January 14, 2026. Descriptive statistics were used to summarize quantitative outcomes. Eight studies published between 2018 and 2025 met the inclusion criteria. All used deep learning-based approaches to perform grimace score prediction or facial action unit analysis. Training data set sizes varied across the studies, ranging from less than 100 images to more than 70,000 images. Reported performance was generally high, with classification accuracies up to 0.97 and strong correlations between automated and human scores (
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Registered trials
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.