Envision is a home cage research platform designed for research from its inception. It is built to give researchers confidence in every data point and faster start up time for studies when compared to cage monitoring platforms. JAX accomplished this by validating Envision against LENS (Longitudinal End-to-end aNnotated Standard).
AI-detected patterns are only meaningful when grounded in real biological behavior. LENS addresses this by applying rigorous, end-to-end validation to every digital measure, delivering research-ready data from day one.
This purpose-built validation standard embeds real-world complexity into how models are developed and evaluated. Built on continuous study data and tested end-to-end, LENS ensures digital measures are proven across true experimental conditions which enable early detection of change, high reproducibility, and confidence in every result.
Validated across diverse strains, coat colors, lighting conditions, group housing configurations, and enrichment environments, LENS reflects the full variability of real-world research.
Activity with Best-Case Validation Data
Activity with Envision LENS Validation
Percent time climbing determined by algorithm validated against “ideal curated data” (top) vs. Envision (bottom), shown for homogenous cages (all saline, all LPS) and mixed cages (saline and LPS treated housed together).
LENS was developed using continuous recordings from real home-cage studies to capture natural, undisturbed behavior over time. Rather than relying on curated or staged examples, it reflects how biology actually occurs across the full 24/7 study cycle, including social interactions and behavioral transitions.
The LENS dataset captures the full variability researchers encounter every day, including differences in strain, coat color, lighting cycles, cage density, and enrichment. It includes both common and rare behaviors, as well as challenging scenarios such as overlapping animals and occlusions, where individuals may be partially or fully hidden from view.
Validated against the full spectrum of real-world conditions rather than best-case examples ensures models are robust, unbiased, and able to perform reliably across the complexity of actual experiments.
All data within LENS is annotated by behavioral experts, establishing a high-quality reference for real-world animal behavior. The rigorous expert informed validation of this comprehensive data set enables digital measures to remain accurate and reliable even under the complex conditions found in real experiments.
Validating all outputs from end-to-end in advance ensures that what researchers using Envision see reflects true biological behavior under real experimental conditions, not idealized test scenarios.
LENS is designed to grow. As new study data is added, models are retrained, re-evaluated, and improved through this same rigorous validation process before updates are released. This ensures Envision remains robust, adaptable, and aligned with evolving research needs.
This product sheet explains the data, machine learning, and end-to-end validation framework behind Envision, showing how continuous home-cage monitoring delivers robust, reproducible insights into animal biology.
This white paper highlights how the JAX Envision platform enables continuous experimental monitoring with highly accurate detection and tracking of individual mice in a cage for weeks to months at a time.
This validation white paper establishes Envision’s activity classifier as a reliable and objective solution for automated mouse behavior classification, enhancing sensitivity, biological relevance, and data interpretability to improve disease monitoring and health assessment.
