ArticleCell reports methods2025
A machine learning tool with light-based image analysis for automatic classification of 3D pain behaviors.
Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Does advancement in marker-less pose-estimation mean more quality research? A systematic review.Frontiers in behavioral neuroscience · 2025Pooled it
- Deep Learning-Based Quantification of Spontaneous Pain Behaviours in Mice.European journal of pain (London, England) · 2026Article
- Article
- Mapping the Extended Pain Pathway: Human Genetic and Multi-Omic Strategies for Next-Generation Analgesics.International journal of molecular sciences · 2026Review
- Preclinical validation of AAV9-TECPR2 gene therapy in a novel knock-in model of TECPR2-related disorder.bioRxiv : the preprint server for biology · 2026Article
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8 authors.
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Abstract
Detailed assessment of pain-related behaviors in animals is essential for both exploring pain mechanisms and evaluating analgesic efficacy. While pose estimation tools have advanced automated behavior analysis, current existing algorithms often do not account for an animal's body-part contact intensity with-and distance from-the surface, a critical nuance for measuring certain pain-related responses like paw withdrawal ("flinching"). These subtle responses continue to require time-consuming and subjective human scoring. Here, we present BAREfoot (behavior with automatic recognition and evaluation), a supervised machine learning (ML) algorithm that combines pose estimation with light-based analysis of body-part contact and elevation to automatically detect pain behaviors in freely moving mice. We show the utility and accuracy of this algorithm for capturing a range of pain-related behavioral bouts using a bottom-up animal behavior platform and its application for robust drug screening. This open-source algorithm is adaptable for detecting diverse behaviors across species and experimental platforms.
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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.