Endometrial Receptivity Prediction During in Vitro Fertilization Using Artificial Intelligence

Endometrial Receptivity Prediction During in Vitro Fertilization Using Artificial Intelligence Analysis of Vaginal Ultrasound Images

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the study is voluntary, involves no additional testing or intervention beyond routine procedures, and consent can be withdrawn verbally or in writing at any time without cause or adverse consequences.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

Study Overview

• Status: Recruiting
• Conditions: Infertility; Infertility (IVF Patients)

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.

• Study Type: Observational
• Enrollment (Estimated): 1500

Contacts and Locations

• Study Contact: Name: Marton Jozsef Kolossvary; Phone Number: +36301789227; Email: marton.kolossvary@gokvi.hu

Study Locations

• Hungary
  • Budapest, Hungary, 1205
    • Recruiting
    • Dunamenti REK Reprodukcios Kozpont
    • Contact: Gabor Spanik; Phone Number: +36702478175; Email: spanik.gabor@dunamentirek.hu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Patients who volunteer for IVF treatment at participating infertility centres

Description

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Clinical pregnancy	Clinical pregnancy: pregnancy can be confirmed through ultrasound visualization of the gestational sac or heartbeat	The date of the ultrasound confirmed clinical pregnancy scheduled at 28 days following embryo transfer

Collaborators and Investigators

• Sponsor: Gottsegen National Cardiovascular Institute
• Collaborators: Dunamenti REK Reprodukcios Kozpont

Study record dates

Study Major Dates

• Study Start (Actual): June 1, 2025
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2028

Study Registration Dates

• First Submitted: December 1, 2024
• First Submitted That Met QC Criteria: December 1, 2024
• First Posted (Actual): December 5, 2024

Study Record Updates

• Last Update Posted (Estimated): September 11, 2025
• Last Update Submitted That Met QC Criteria: September 10, 2025
• Last Verified: September 1, 2025

More Information

Terms related to this study

• Keywords: Ultrasound imaging; Endometrial receptivity; assisted reproduction; Embryo implantation; Artificial intelligence (AI)
• Additional Relevant MeSH Terms: Urogenital Diseases; Genital Diseases; Infertility
• Other Study ID Numbers: endoAI

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No