Using AI to predict uterine lining receptivity during IVF
Official title: Endometrial Receptivity Prediction During in Vitro Fertilization Using Artificial Intelligence Analysis of Vaginal Ultrasound Images
Run by Gottsegen National Cardiovascular Institute · NCT06717802 · Data updated 2026-11-12
This project will try using artificial intelligence to read routine vaginal ultrasound images of the uterine lining to see if it can predict implantation success for people aged 18–40 undergoing IVF with a single blastocyst transfer.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 40 Years |
| Sex | Women only |
| Enrollment | 1500 (estimated) |
| Sponsor | Gottsegen National Cardiovascular Institute Academic / other |
| Locations | 1 site (Budapest) |
| Status | Recruiting |
| Started | June 1, 2025 |
| Main results expected | December 31, 2027 |
| Study ends | December 31, 2028 |
| Registry record updated | September 11, 2025 |
| Trial ID | NCT06717802 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
People aged 18–40 undergoing IVF who plan a single blastocyst transfer, have no more than three prior unsuccessful transfers, and do not have excluded uterine or infectious conditions are ideal candidates.
✕Probably not a fit
Patients with uterine malformations, fibroids, adenomyosis, Asherman syndrome, hydrosalpinx, endometriosis, planned freeze-all cycles, or positive hepatitis B/C or HIV tests would be excluded and not benefit from this project.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This is an observational study that will analyze routine vaginal ultrasound images and clinical data collected during standard IVF cycles using an artificial intelligence algorithm. About 1,500 consenting patients aged 18–40 who are indicated for IVF and plan single blastocyst transfer will be enrolled over three years. No extra tests or clinic visits are required beyond usual care; researchers will use existing image and outcome data such as endometrial thickness, stimulation details, and pregnancy results. The goal is to identify imaging patterns the AI can use to predict endometrial receptivity and implantation outcomes.
Why it matters
Potential benefit: If successful, the tool could help clinicians identify cycles with a more receptive uterine lining and improve timing or selection for embryo transfer, potentially increasing IVF pregnancy rates and reducing unsuccessful cycles.
How similar studies have gone: AI has shown promise for embryo selection and for analyzing imaging patterns, but a reliable, widely adopted AI method for predicting endometrial receptivity from routine ultrasound has not yet been established.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Female patient 18-40 years, for whom IVF is indicated * Maximum 3 unsuccessful previous embryo transfers * Only cycles in which single blastocyst is transferred Exclusion Criteria: * Congenital uterine anomalies, fibroids, adenomyosis, Asherman-syndrome or any other conditions resulting in malformation of the uterus * Presence of hydrosalpinx * Endometriosis * Planned freeze-all cycle * Positive hepatitis B, hepatitis C or HIV screening test
Where it's running
-
Dunamenti REK Reprodukcios Kozpont
Budapest, Hungary Recruiting
Study contacts
- Study coordinator: Marton Jozsef Kolossvary
- Email: marton.kolossvary@gokvi.hu
- Phone: +36301789227
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.