Diabetes mellitus, a chronic condition affecting millions of people worldwide, is characterised by the body's inability to regulate blood glucose levels independently. The prevalent forms include type 1, type 2, and gestational diabetes, each necessitating distinct management strategies. This article focuses on type 1 diabetes, particularly the challenges faced by female patients due to menstrual cycle-induced variations in insulin sensitivity. An extended Kalman filter, applied within the Bergman Minimal Model framework, is proposed for estimating unmeasured state variables crucial for effective diabetes management. The study underscores the impact of menstrual cycle phases on insulin sensitivity, highlighting the need for tailored insulin administration strategies to maintain optimal glucose levels. Through simulation studies based on a two-compartment model for insulin and glucose dynamics, the potential of Kalman filtering to enhance the knowledge about the influence of the insulin sensitivity for female type 1 diabetes patients is demonstrated.