Machine Learning and Pregnancy Success Prediction in Fertility Treatments

NCT06884930 | Active Not Recruiting

• 1 other identifier: MaLIV-PMA
• Study Type: observational
• Target: 5,000
• Locations: 1 country
• Sites: 1

Brief 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)

Keywords

IVF; Machine Learning-based Predictive Model; pregnancy

Outcome Measures

Primary Outcomes (1)

• Pregnancy rate

  The primary endpoint of the study will be the clinical pregnancy defined as a pregnancy confirmed by an increasing level of hCG and the presence of a gestational sac or heartbeat detected by ultrasound.

  Data will be extracted for all ART cycles conducted between 2019 and 2024 to allow for the comprehensive development of the Machine Learning-based model.

Study Arms (1)

IVF patients

Eligibility Criteria

• Age: 18 Years - 43 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Couples who received IVF treatment between 2019 and 2024

You may qualify if:

• Patients who underwent ART procedures, including IVF and ICSI, between 2019 and 2024.
• Women aged between 18 and 43 years.

You may not qualify if:

• Patiens with incomplete or missing data records that do not provide sufficient information for analysis.
• women outside the 18 to 43 age range

Sponsors & Collaborators

• IRCCS San Raffaele: lead
• Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico: collaborator
• ASST Grande Ospedale Metropolitano Niguarda: collaborator

Study Sites (1)

IRCCS San Raffaele Hospital

Milan, Milano, 20132, Italy

Location

Related Publications (5)

• Zhang Q, Liang X, Chen Z. A review of artificial intelligence applications in in vitro fertilization. J Assist Reprod Genet. 2025 Jan;42(1):3-14. doi: 10.1007/s10815-024-03284-6. Epub 2024 Oct 14.

  PMID: 39400647 RESULT

• Jiang VS, Bormann CL. Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade. Fertil Steril. 2023 Jul;120(1):17-23. doi: 10.1016/j.fertnstert.2023.05.149. Epub 2023 May 19.

  PMID: 37211062 RESULT

• Attività del Registro Nazionale Italiano della Procreazione Medicalmente Assistita - 17° Report 2021

  RESULT

• European IVF Monitoring Consortium (EIM) for the European Society of Human Reproduction and Embryology (ESHRE); Smeenk J, Wyns C, De Geyter C, Kupka M, Bergh C, Cuevas Saiz I, De Neubourg D, Rezabek K, Tandler-Schneider A, Rugescu I, Goossens V. ART in Europe, 2019: results generated from European registries by ESHREdagger. Hum Reprod. 2023 Dec 4;38(12):2321-2338. doi: 10.1093/humrep/dead197.

  PMID: 37847771 RESULT

• Infertility prevalence estimates, 1990-2021. Geneva: World Health Organization; 2023. Licence: CC BY-NC-SA 3.0 IGO.

  RESULT

MeSH Terms

Conditions

Infertility

Condition Hierarchy (Ancestors)

Genital Diseases; Urogenital Diseases

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: MD

Study Record Dates

• First Submitted: March 13, 2025
• First Posted: March 19, 2025
• Study Start: April 16, 2025
• Primary Completion: March 1, 2026
• Study Completion: March 1, 2026
• Last Updated: October 7, 2025

Record last verified: 2025-10

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)