Addressing the Preclinical Reproducibility Crisis Using Digital Home Cage Monitoring

Researchers using animal models frequently encounter variability in their study results due to limitations in data collection methodologies. These methods often require human interpretation of behavioral and physiological cues through direct observation or handling of mice, leading to lack of replicability that can be further exacerbated when different researchers conduct the same study.

In this webinar, Dr. Michael Saul, Computational Scientist at The Jackson Laboratory, will discuss recent studies using the Envision™ platform that show how AI-powered, digital home cage-monitoring of mice with minimal disturbance creates opportunities to reduce study variability. Dr. Saul will guide attendees through datasets demonstrating how Envision helps research cut through the "noise" to uncover key study outcomes that influence decision-making.