Ovarian hyperstimulation is a critical component of in vitro fertilization (IVF), yet patient responses remain highly variable, and estradiol, the current biomarker for follicular maturation, lacks sufficient predictive power. This project integrates statistical analysis of steroid hormone profiles with computational modeling to address two major IVF challenges: understanding patient heterogeneity in ovarian hyperstimulation and improving biomarker-based predictions of follicular maturation. The objective is to identify key factors influencing response to the stimulation and assess an alternative biomarker that may outperform currently used estradiol. The findings will be incorporated into a mechanistic model of ovarian follicle growth, enhancing its predictive accuracy. The resulting hybrid model, combining statistical and mechanistic components, will serve as a foundation for developing personalized ovarian stimulation protocols, ultimately improving IVF treatment outcomes.

Keywords
computational modeling; statistical analysis; hybrid model; biormarker identification; in vitro fertilization; IVF

26-21654O

English

Musilová Jana, Ing., Ph.D. - principal person responsible

Department of Biomedical Engineering
- responsible department (12.3.2025 - not assigned)
Department of Biomedical Engineering
- beneficiary (12.3.2025 - not assigned)

Responsibility: Musilová Jana, Ing., Ph.D.