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Machine Learning and Pregnancy Success Prediction in Fertility Treatments

NCT06884930 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 5000

Last updated 2025-10-07

No results posted yet for this study

Summary

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.

Conditions

  • Infertility (IVF Patients)

Sponsors & Collaborators

  • Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico

    collaborator OTHER

  • ASST Grande Ospedale Metropolitano Niguarda

    collaborator OTHER

  • IRCCS San Raffaele

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
43 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-04-16
Primary Completion
2026-03-31
Completion
2026-03-31

Countries

  • Italy

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06884930 on ClinicalTrials.gov