Research of Predictive Factors of in Vitro Fertilization (IVF) Outcome AI-IVF Study

Research of Predictive Factors of in Vitro Fertilization (IVF) Outcome in Women Affected by Autoimmune Disorders: From Biological Markers to Artificial Intelligence

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06872294

Acronym

AI-IVF

Enrollment

200

Registered

2025-03-12

Start date

2024-11-22

Completion date

2025-12-31

Last updated

2025-03-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Autoimmune Diseases

Brief summary

Infertility is the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected intercourse, while subfertility means a delay in achieving pregnancy. Several factors can contribute to these conditions, such as autoimmune diseases like systemic thyroid and celiac diseases, rheumatoid arthritis, systemic lupus erythematosus, and endometriosis. Some of them may overlap, sharing a common immunological milieu. In this scenario, medically ART can help women carry on pregnancies if they are in stable disease remission and receive adequate treatment. However, the number of oocytes that can be ovulated under stimulation is highly variable, so the outcome of any IVF is linked to many factors, not only biological but also connected to the mean success rate of the fertility center. For these reasons, the need to find methods for identifying the best gametes and embryos to guarantee a successful pregnancy is becoming pressing. AI can be essential to develop prediction models for pregnancy outcomes for patients undergoing ART treatments. AI currently represents a modern way to obtain algorithms from a set of observable data that could help clinical decisions

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

FEMALE

Age

18 Years to 43 Years

Healthy volunteers

Inclusion criteria

* Women with age below 43 years old * IVF cycle with fresh semen sample that could be used * progressive motile spermatozoa expected count of minimum 1 million following density gradient purification on the day of oocyte pick up

Exclusion criteria

* Women with uterine abnormalities * Women with chromosomal and genetic abnormalities * Positivity to serological test (HCV, HBV, HIV, VDRL-TPHA)

Design outcomes

Primary

Measure	Time frame	Description
Predictive algorithm creation	through study completion, an average of 2 years	Probability to achieve a pregnancy

Countries

Italy

Contacts

Primary ContactClaudia Omes, PhD

c.omes@smatteo.pv.it+390382503238

Outcome results