A machine-vision-based frailty index for mice
Nature Aging. 2022;2(8):756-66
This work was funded by The Jackson Laboratory Directors Innovation Fund, National Institute of Health, DA041668 (V.K.) and DA048634 (V.K.) and Nathan Shock Centers of Excellence in the Basic Biology of Aging, AG38070 (G.A.C.).
Heterogeneity in biological aging manifests itself in health status and mortality. Frailty indices (FIs) capture health status in humans and model organisms. To accelerate our understanding of biological aging and carry out scalable interventional stud- ies, high-throughput approaches are necessary. Here we introduce a machine-learning-based visual FI for mice that operates on video data from an open-field assay. We use machine vision to extract morphometric, gait and other behavioral features that correlate with FI score and age. We use these features to train a regression model that accurately predicts the normalized FI score within 0.04 ± 0.002 (mean absolute error). Unnormalized, this error is 1.08 ± 0.05, which is comparable to one FI item being mis-scored by 1 point or two FI items mis-scored by 0.5 points. This visual FI provides increased reproducibility and scal- ability that will enable large-scale mechanistic and interventional studies of aging in mice.
A machine-vision-based frailty index for mice Nature Aging. 2022;2(8):756-66