Machine-Learning Prediction of Pregnancy Outcomes After Assisted Reproductive Treatments
The goal of this observational study is to develop and validate a machine-learning model to predict pregnancy outcomes in women with a positive pregnancy test following assisted reproductive treatment (ART), with the primary analysis focusing on in vitro fertilization (IVF) and frozen embryo transfer (FET). The study will also include an exploratory analysis of pregnancies following intrauterine insemination (IUI). The main questions addressed by the study are: Can early blood levels of beta-human chorionic gonadotropin (beta-hCG) predict the likelihood of live birth in an IVF or FET cycle? Can early beta-hCG levels help identify pregnancies at increased risk of biochemical pregnancy or early pregnancy loss? Participants will have clinical and treatment information, including beta-hCG measurements and pregnancy outcomes, collected from medical records or during prospective follow-up. No changes to participants' medical treatment will be made as part of the study.
Women who underwent ART procedures (IUI, IVF/ICSI, or FET) using homologous oocy... [+5]
Missing beta-HCG measurements critical for model input. [+5]