A machine learning approach for forecasting follicle growth and predicting the optimal day of trigger during ovarian stimulation

Objective: To develop a machine learning approach for forecasting follicle growth and predicting the optimal day of trigger in terms of mature eggs retrieved during ovarian stimulation.

Impact Statement: We have developed a machine learning approach for forecasting E2 and follicle growth and predicting the number of MII eggs retrieved during ovarian stimulation, which may help with the decision of triggering "today vs. tomorrow".