Project Objectives
- Promote a minimally invasive estrous‑cycle monitoring method combining tunnel handling and vaginal lavage.
- Digitize and annotate thousands of images relating to estrous cycle stage for algorithm training.
- Develop an AI‑based, user‑friendly tool for for determining mouse estrous cycle stages.
- Ensure generalizability across mouse and rat strains.
- Provide open‑source datasets, protocols, and educational materials.
- Organize workshops for hands‑on training.
How This Advances 3Rs Implementation
- Enables refinement across laboratories through an accessible, standardized method.
- Supports reduction through lower experimental variability.
- Provides in‑silico training resources, which reduce animal use for education.
- Strengthens reproducibility, transparency, and scientific integrity.
- Facilitates the reliable inclusion of female rodents across disciplines.
Background
Female rodents have been excluded from research studies for decades due to concerns over hormonal variability during their estrous cycle. It has led to oversights in crucial sex differences, translational failures, and the consequential waste and suffering of animals used for inconclusive research. However, as most funders now require the use of sex as a biological variable, researchers need reliable, ethical, and efficient ways to assess the estrous cycle of female rodents, because existing methods can be stressful, invasive, and inconsistent.
This project aims to promote a minimally invasive method for estrous cycle determination that combines tunnel handling and vaginal lavage. Further to this, the project also aims to establish and promote an automated, cloud‑based method for determining estrous cycle stages using deep learning AI. This approach allows researchers to consider endocrine states in rodent studies much more efficiently, which both improves data interpretability, and advances our understanding of sex differences.
