Letterie G, et al. - A computer algorithm designed for in vitro fertilization (IVF) management was described and was investigated for accuracy in the day-to-day decision making during ovarian stimulation for IVF when compared with evidence-based decisions by the clinical team. Researchers derived data from monitoring during ovarian stimulation from IVF cycles. The database comprised 2,603 cycles (1,853 autologous and 750 donor cycles) including 7,376 visits for training. For challenge and calculation of accuracy, use of an additional 556 unique cycles was made. There was a total of 59,706 data points. Assessment of accuracy of the algorithm to prognosticate four critical clinical decisions during ovarian stimulation for IVF was done: [1] stop stimulation or continue stimulation. If the decision was to stop, then the next automated decision was to [2] trigger or cancel. If the decision was to return, then the next key decisions were [3] number of days to follow-up and [4] whether any dosage adjustment was required. For these four decisions, they noted algorithm accuracies as follows: continue or stop treatment: 0.92; trigger and schedule oocyte retrieval or cancel cycle: 0.96; dose of medication adjustment: 0.82; and number of days to follow-up: 0.87. Herein a first iteration of a predictive analytic algorithm was defined that was shown to be highly accurate as well as in agreement with evidence-based decisions by expert teams during ovarian stimulation during IVF. These tools provide a potential platform to improve clinical decision making during IVF.