Uncertainty in Clinical Data and Stochastic Model for In-vitro Fertilization

In-vitro Fertilization (IVF) is the most common technique in Assisted Reproductive Technology (ART). It has been divided into four stages; (i) superovulation, (ii) egg retrieval, (iii) insemination/fertilization and (iv) embryo transfer. The first stage of superovulation is a drug induced method to enable multiple ovulation, i.e., multiple follicle growth to oocytes or matured follicles in a single menstrual cycle. IVF being a medical procedure that aims at manipulating the biological functions in the human body is subjected to inherent sources of uncertainty and variability. Also, the interplay of the hormones with the natural functioning of the ovaries make the procedure dependent on several factors like patient's condition in terms of cause of infertility, actual ovarian function, responsiveness to medication, etc. The treatment requires continuous monitoring and testing and this can give rise to errors in observations. Thus, it becomes essential to look at the process noise and deviations and think of a way to account for them to build better representative models for follicle growth. The purpose of this work is to come up with a robust model which can project the superovulation cycle outcome based on the hormonal doses and patient response in presence of uncertainty. The customized stochastic model results in better projection of the cycle outcome for the patients where the deterministic model has some deviations from the clinical observations and the growth term value is not within the range of '0.3 to 0.6'. It was found that the prediction accuracy was enhanced by more than 70% for some patients by using the stochastic model projections.