Personalizing the first dose of FSH for IVF/ICSI patients through machine learning: a non-inferiority study protocol for a multi-center randomized controlled trial.

Background: Adequately selecting the initial follicle-stimulating hormone (FSH) dose during controlled ovarian stimulation (COS) is key for success in assisted reproduction. The objective of COS is to obtain an optimal number of oocytes to increase the chances of achieving a pregnancy, while avoiding complications for the patient. Current clinical protocols do achieve good results for the majority of patients, but further refinements in individualized FSH dosing may reduce the risk of poor ovarian response while also limiting the risk of ovarian hyperstimulation syndrome (OHSS) risk.

Supporting first FSH dosage for ovarian stimulation with machine learning.

Research Question: Is it possible to identify accurately the optimal first dose of FSH in ovarian stimulation by means of a machine learning model?

Design: Observational study (2011-2021) including first IVF cycles with own oocytes. A total of 2713 patients from five private reproductive centres were included in the development phase (2011-2019) and 774 in the validation phase (2020-2021). Predictor variables included age, BMI, AMH, AFC and previous live births.