Endometrial Receptivity Prediction During in Vitro Fertilization Using Artificial Intelligence

NCT ID: NCT06717802

Last Updated: 2025-09-11

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 1500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-06-01
• Study Completion Date: 2028-12-31

Brief Summary

The investigators plan to use artificial intelligence to analyse vaginal ultrasound images of the uterine lining (endometrium) taken during routine IVF treatment, which may predict implantation success during IVF treatment.

Participation in the study is voluntary, involves no additional testing or intervention beyond routine procedures, and consent can be withdrawn verbally or in writing at any time without cause or adverse consequences.

Over a three-year period, the trial is expected to enrol approximately 1,500 patients between the ages of 18 and 40 who are indicated for IVF treatment and who volunteer for treatment.

Patients enrolled in the study will not be required to attend more clinic visits during treatment than they would otherwise have to. During the trial, certain patient-specific data (age, indication for treatment, body mass index), stimulation-specific data (duration of stimulation, type and dose of drug, endometrial thickness), ultrasound scans and outcome-specific data (treatment failure, biochemical pregnancy, clinical pregnancy) will be collected. The data will be stored in a secure database. The data collected during the study will only be accessible to the professionals involved in the study and no information, including personal data, will be disclosed to third parties.

Detailed Description

Infertility affects one in six people globally over the duration of their reproductive lives. For successful IVF treatment, three essential things are needed: a euploid embryo, receptive endometrium and in case of frozen embryos, the exact timing of the transfer. Several methods are available for evaluating embryos, such as: microscopic evaluation (e.g. Gardner grade), genetic tests (PGT), biochemical tests (non-invasive PGT). Furthermore, morphological and morphokinetic evaluation is possible using time-lapse video surveillance systems that are evaluated using artificial intelligence. The most common method for testing the receptivity of the endometrium is the determination of the largest diameter measured in the mid-sagittal plane. A thickness between 7 and 10 mm is considered the cut-off for successful implantation. Further studies were conducted on the level of the serum and endometrial progesterone, the volume, echogenicity, peristalsis of the endometrium, the flow conditions in it and the success of implantation. The predictive value of hysteroscopy, 3D ultrasound and the role of ultrasound elastography to predict pregnancy outcome has been investigated. The above-mentioned tests for endometrial receptivity have not been widely used in practice, and there is currently no method for predicting endometrial receptivity in everyday practice.

As the quality of imaging has improved, the focus has shifted to analyzing the pattern of the endometrium, the latest research using artificial intelligence.

The aim of this study is to investigate whether segmentation and analysis of the endometrium using artificial intelligence in vaginal ultrasound images taken during stimulation and on the day of transfer can help to more accurately determine the receptivity of the endometrium. The significance of this is that in case of poor implantation chances it is possible to freeze the embryo(s) and have the opportunity to implant the embryo in a subsequent natural or hormone replacement therapy cycle, possibly with better chances.

Conditions

Infertility; Infertility (IVF Patients)

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Eligibility 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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 40 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

Dunamenti REK Reprodukcios Kozpont

UNKNOWN

Sponsor Role: collaborator

Gottsegen National Cardiovascular Institute

OTHER

Sponsor Role: lead

Responsible Party

Márton Kolossváry

Head of Education and Research

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Dunamenti REK Reprodukcios Kozpont

Budapest, , Hungary

Site Status: RECRUITING

Countries

Hungary

Central Contacts

Marton Jozsef Kolossvary

Role: CONTACT

marton.kolossvary@gokvi.hu

+36301789227

Facility Contacts

Gabor Spanik

Role: primary

spanik.gabor@dunamentirek.hu

+36702478175

Other Identifiers

endoAI

Identifier Type: -

Identifier Source: org_study_id