Using Machine Learning and Predictive Artificial Intelligence to Determine Cage Change Frequency for Mice Housed in Individually Ventilated Cages and Drive Vivarium Operational Efficiency.

Abstract

A standard 2-wk cage change frequency for individually ventilated mouse cages is used in many research facilities, with negligible effects on animal health and welfare. However, these techniques rely on subjective visual evaluations and often require spot changes. In this study, we describe the use and validation of digital monitoring technology to objectively determine the necessity of a cage change for mice. We used a machine learning/artificial intelligence algorithm that was trained by annotating human observations of soiled bedding to correlate with the Bedding Status Index (BSI), a digital measure quantifying bedding 'wetness.' Training of the algorithm was performed using various mouse strains of different age, sex, and cage densities to account for variability of these factors. Through constant user feedback and increased datasets, we were able to identify soiled cages with an accuracy >90% for cages with higher densities (for example, 5 animals per cage), while lower densities exhibited slightly reduced accuracy levels (the lowest accuracy was attributed to single-housed mice, at 76%). Our data show that the average change intervals for most average-sized mice ranged between 3 and 6 wk depending on the number of animals in the cage, which is significantly different from the standard 2-wk change used in our facility. Retired breeders and larger mice tended to have a shorter cage change interval as determined by the algorithm. These results show that the Bedding Status Index, which measures an intracage environmental variable, namely bedding wetness, can be used as a marker for cage change. The extended cage change schedule did not affect intracage ammonia, CO2 levels, mouse growth rates, or circadian rhythm metrics. Using digital alerts to determine the need for a cage change resulted in a 65% to 70% reduction in the number of cage changes needed, indicating that this method can improve operational efficiency by reducing cage changes, cage wash time, staff labor, and resource consumption.