AIMS

• Recognizing the rodent estrous cycle as a crucial welfare indicator faces challenges due to a historical bias toward male animals in the scientific community.
• The project aims to empower investigators to consider female endocrine states effectively by i) promoting a non-invasive method for estrous cycle determination, which combines tunnel handling and vaginal lavage, and ii) establishing an automated user-friendly method using deep learning AI for rapid estrous cycle staging.
• This will enhance our ability to consider endocrine states in rodent studies, improving data interpretability, precision, and advancing understanding of sex differences, all within a refined and noninvasive framework.