Machine vision-based frailty assessment for genetically diverse mice.
Document Type
Article
Publication Date
8-1-2025
Original Citation
Sabnis G,
Churchill G,
Kumar V.
Machine vision-based frailty assessment for genetically diverse mice. Geroscience. 2025;47(4):5435–48.
Keywords
JMG, Animals, Frailty, Mice, Mice, Inbred C57BL, Aging, Male, Female, Behavior, Animal
JAX Source
Geroscience. 2025;47(4):5435–48.
ISSN
2509-2723
PMID
40095188
DOI
https://doi.org/10.1007/s11357-025-01583-z
Grant
This work was funded by The Jack- son Laboratory Directors Innovation Fund, National Institute of Health AG078530 (NIA, V.K.), DA041668 and DA048634 (NIDA, V.K.), and Nathan Shock Centers of Excellence in the Basic Biology of Aging AG38070 (NIA, G.C.).
Abstract
Frailty indexes (FIs) capture health status in humans and model organisms. To accelerate our understanding of biological aging and carry out scalable interventional studies, high-throughput approaches are necessary. We previously introduced a machine vision-based visual frailty index (vFI) that uses mouse behavior in the open field to assess frailty using C57BL/6J (B6J) data. Aging trajectories are highly genetic and are frequently modeled in genetically diverse animals. In order to extend the vFI to genetically diverse mouse populations, we collect frailty and behavior data on a large cohort of aged Diversity Outbred (DO) mice. Combined with previous data, this represents one of the largest video-based aging behavior datasets to date. Using these data, we build accurate predictive models of frailty, chronological age, and even the proportion of life lived. The extension of automated and objective frailty assessment tools to genetically diverse mice will enable better modeling of aging mechanisms and enable high-throughput interventional aging studies.