We propose to develop a software for clinical use that will provide better image analysis tools for clinicians, as well as data- and model-based guidance regarding the best IVF treatment schedule, based on the measured hormone levels and the ovarian response. Given our detailed understanding of ovarian follicle maturation, this personalized medicine approach can go beyond statistical correlation and can be based on a mechanistic understanding of the hormonal control of ovarian follicle maturation.
First successfully used in 1978, in vitro fertilization (IVF) is now used routinely as a form of assisted reproductive technology (ART). While the cumulative likelihood of live birth by the fifth embryo transfer cycle is currently about 80%, there remains an urgent need to improve treatment protocols, considering the high financial and psychological burden of the many failed IVF cycles. The main determinants of live birth are age and ovarian reserve, but individual differences in hormone levels strongly affect IVF outcome.
