Machine Learning and Pregnancy Success Prediction in Fertility Treatments

Infertility, as defined by the World Health Organization (WHO), is a disorder of the male or female reproductive system characterized by the inability to achieve a clinical pregnancy after 12 months or more of regular, unprotected sexual intercourse. In modern fertility treatment, assisted reproductive technologies (ART), including in vitro fertilization (IVF), have become a standard approach for addressing complex fertility issues and sterility. In Italy, infertility affects approximately 16.5% of couples. Despite advancements in ART, comparing the failure rates of pregnancies achieved through ART with those of spontaneous pregnancies in Italy reveals significant differences, particularly in terms of success rates, miscarriage rates, and embryo implantation outcomes. In this context, AI-based models have shown promising potential in predicting IVF success by analyzing complex datasets that include patient demographics, hormonal levels, and embryo morphology. Research indicates that AI can enhance embryo selection, predict the optimal timing for embryo transfer, and advance personalized medicine approaches in reproductive health. This study aims to use of Machine Learning to identify patterns and factors associated with successful pregnancy outcomes by analyzing large-scale, anonymized ART data. The resulting predictive model could enable clinicians to better personalize treatment protocols for each patient, optimizing medication dosages, timing, and embryo selection. It could also improve pregnancy success rates while reducing the emotional and financial burden on patients, thus advancing the standard of care in ART.

NCT Number	NCT06884930
Lead Sponsor	IRCCS San Raffaele Collaborators: Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, ASST Grande Ospedale Metropolitano Niguarda
Conditions	Infertility (IVF Patients)
Enrollment	5,000 participants
Start Date	2025-04-16
Primary Completion	2026-03 (estimated)
Study Completion	2026-03 (estimated)
Updated on ClinicalTrials.gov	2025-10-07