Refining estrous cycle through non-invasive collection procedures and deep learning classification

Keywords: non-invasive monitoring of estrous cycle, deep learning system for classification of estrous stage, tunnel handling, examination of vaginal lavage, Sex as a biological variable (SABV) policy, female rodents
Budget: CHF 20'000.-

Aim

• 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.