Development of PainFace software to simplify, standardize, and scale up mouse grimace analyses.
Abstrak
ABSTRACT Facial 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.
Topik & Kata Kunci
Penulis (26)
Eric S McCoy
Sang Kyoon Park
Rahul Patel
Dan F Ryan
Zachary J. Mullen
Jacob J. Nesbitt
Josh E. Lopez
B. Taylor-Blake
Kelly A. Vanden
James L. Krantz
Wenxin Hu
Rosanna L. Garris
Magdalyn G Snyder
L. V. Lima
S. Sotocinal
J. Austin
Adam D. Kashlan
Sanya Shah
Abigail K. Trocinski
Samhitha S. Pudipeddi
Rami M. Major
Hannah O. Bazick
Morgan R Klein
J. Mogil
Guorong Wu
M. Zylka
Akses Cepat
PDF tidak tersedia langsung
Cek di sumber asli →- Tahun Terbit
- 2024
- Bahasa
- en
- Total Sitasi
- 26×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1097/j.pain.0000000000003187
- Akses
- Open Access ✓