Multiple or double embryo transfer is a common strategy to increase the pregnancy rate after in vitro fertilization (IVF). But it also created increased multiple pregnancy rate. Twin pregnancy resulted in high risk pregnancy such as preeclampsia, gestational diabetes mellitus, preterm labor, Cesarean Section delivery. The pregnancy complication and deteriorated newborn health after twin pregnancy cause more medical cost, economical cost, and profound parental psychosocial stress. In order to decrease twin pregnancy risk and sustain optimal pregnancy rate by choosing suitable number of embryos during transfer, we developed an artificial intelligence (AI) prediction system applied in clinical practice. This AI prediction model was built from our IVF dataset and provided reliable outcome prediction of the pregnancy rate and twin pregnancy risk after IVF-ET. The AI model was then integrated with our electronic medical system (HIS system), so the outcome prediction could be real-time and personalized. In clinical utility, implication of the AI predictive system could help clinicians and infertile couples choose suitable number of embryos during transfer, decrease multiple pregnancy, and sustain optimal pregnancy rate.

The AI model was then integrated with our electronic medical system (HIS system), so the outcome prediction could be real-time and personalized. In clinical utility, implication of the AI predictive system could help clinicians and infertile couples choose suitable number of embryos during transfer, decrease multiple pregnancy, and sustain optimal pregnancy rate.