LAUSR

Objective Baseline metrics such as age and hormone levels are used to counsel patients undergoing IVF. For patients who fail multiple cycles, counseling can become more challenging and cycle-level metrics become an important consideration. As datasets grow, artificial intelligence (AI) systems are augmenting the diagnostic and prognostic capabilities of the human brain. The limitation is that these systems are only as smart as the input data they are trained on. However, the advantage is that they are unbiased, bring greater standardization to counseling, and often yield nonobvious insights. Here, we applied AI methods as a proof-of-concept and to better understand trends after failed IVF cycles.