Semi-automated generation of pictures for the Mouse Grimace Scale: A multi-laboratory analysis (Part 2)

Ernst, Lisa; Kopaczka, Marcin; Schulz, Mareike; Talbot, Steven R.; Struve, Birgitta; Haeger, Christine; Bleich, Andre; Durst, Mattea; Jirkof, Paulin; Arras, Margarete; van Dijk, Roelof Maarten; Mitjanovic, Nina; Potschka, Heidrun; Merhof, Dorit und Tolba, Rene H. (2019): Semi-automated generation of pictures for the Mouse Grimace Scale: A multi-laboratory analysis (Part 2). In: Laboratory Animals, Bd. 54, Nr. 1: S. 92-98

Abstract

The Mouse Grimace Scale (MGS) is an established method for estimating pain in mice during animal studies. Recently, an improved and standardized MGS set-up and an algorithm for automated and blinded output of images for MGS evaluation were introduced. The present study evaluated the application of this standardized set-up and the robustness of the associated algorithm at four facilities in different locations and as part of varied experimental projects. Experiments using the MGS performed at four facilities (F1-F4) were included in the study;200 pictures per facility (100 pictures each rated as positive and negative by the algorithm) were evaluated by three raters for image quality and reliability of the algorithm. In three of the four facilities, sufficient image quality and consistency were demonstrated. Intraclass correlation coefficient, calculated to demonstrate the correlation among raters at the three facilities (F1-F3), showed excellent correlation. The specificity and sensitivity of the results obtained by different raters and the algorithm were analysed using Fisher's exact test (p < 0.05). The analysis indicated a sensitivity of 77% and a specificity of 64%. The results of our study showed that the algorithm demonstrated robust performance at facilities in different locations in accordance with the strict application of our MGS setup.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Tiermedizin
• Fakultätsübergreifende Einrichtungen: Graduate School of Systemic Neurosciences (GSN)
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit ; 500 Naturwissenschaften und Mathematik > 500 Naturwissenschaften
• ISSN: 0023-6772
• Sprache: Englisch