• Eric S McCoy, Sang Kyoon Park, Rahul P Patel, Dan F Ryan, Zachary J Mullen, Jacob J Nesbitt, Josh E Lopez, Bonnie Taylor-Blake, Kelly A Vanden, James L Krantz, Wenxin Hu, Rosanna L Garris, Magdalyn G Snyder, Lucas V Lima, Susana G Sotocinal, Jean-Sebastien Austin, Adam D Kashlan, Sanya Shah, Abigail K Trocinski, Samhitha S Pudipeddi, Rami M Major, Hannah O Bazick, Morgan R Klein, Jeffrey S Mogil, Guorong Wu, and Mark J Zylka.
• UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
• Pain. 2024 Aug 1; 165 (8): 179318051793-1805.

AbstractFacial grimacing is used to quantify spontaneous pain in mice and other mammals, but scoring relies on humans with different levels of proficiency. Here, we developed a cloud-based software platform called PainFace ( http://painface.net ) that uses machine learning to detect 4 facial action units of the mouse grimace scale (orbitals, nose, ears, whiskers) and score facial grimaces of black-coated C57BL/6 male and female mice on a 0 to 8 scale. Platform accuracy was validated in 2 different laboratories, with 3 conditions that evoke grimacing-laparotomy surgery, bilateral hindpaw injection of carrageenan, and intraplantar injection of formalin. PainFace can generate up to 1 grimace score per second from a standard 30 frames/s video, making it possible to quantify facial grimacing over time, and operates at a speed that scales with computing power. By analyzing the frequency distribution of grimace scores, we found that mice spent 7x more time in a "high grimace" state following laparotomy surgery relative to sham surgery controls. Our study shows that PainFace reproducibly quantifies facial grimaces indicative of nonevoked spontaneous pain and enables laboratories to standardize and scale-up facial grimace analyses.Copyright © 2024 International Association for the Study of Pain.

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