Refining animal care through technology: Addressing alopecia in Jaculus jaculus with validated computer vision analysis.

ABSTRACT: We validated the use of an open-source computer vision toolkit to analyze high-quality behavioral data and evaluate welfare in the Lesser Egyptian Jerboa (Jaculus jaculus). Movements of these small, nocturnal rodents are rapid and difficult to observe, potentially obscuring behavioral assessment. However, assessment became warranted when alopecia and jumping were noted. We compared trained human observers to machine learning trained computer vision algorithms, evaluating accuracy and precision in behavioral classification. Human observers categorized behaviors with an overall accuracy of 0.71 ± 0.11 and an intraclass correlation coefficient (ICC) of 0.92 ± 0.07, with greater odds of misidentifying behaviors lasting less than one second. Computer vision classifiers successfully met human-gr

SUBMITTER: Boulanger MD 

PROVIDER: S-EPMC12604758 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature