Utilizing a machine-learning model for determining the optimal trigger days may improve antagonist protocol cycle outcomes across all age groups in freeze-all or fresh transfer cycles. Implementation of these models may more accurately predict the number of oocytes retrieved, thus optimizing physicians' decisions, balancing workloads and creating more standardized, yet patient-specific, protocols.