Aftificial Inteligence in Assisted Reproductive Techniques to Assess Oocyte Quality and Embryo Ploidy (SMARTAI)

January 17, 2025 updated by: Jaromír Mašata, Charles University, Czech Republic

Scanning the Meiotic Spindle in Assisted Reproductive Techniques to Assess Oocyte Quality and Embryo Ploidy Evaluated by Artificial Intelligence (SMARTAI Study)

The assisted reproduction success rate is affected by several factors including the age of the women, oocyte quality and maturation state, as well as sperm quality. Imaging of the meiotic spindle may be crucial for determining the oocyte maturation. Artificial intelligence (AI) will be applied to establish the complex oocyte quality, embryo ploidy and pregnancy success probability from the sequence of data, starting with the recording of the meiotic spindle in polarized light, through paternal factors up to the time lapse recording of early embryo development. This strategy should reduce the cost of fertility treatment thanks to increased efficiency in choosing the most promising candidates and reducing the need for costly laboratory analyses.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

One of the main strategies of infertility treatment is in vitro fertilization (IVF). The IVF success rate is affected by several key factors including the age of the women, oocyte quality and maturation state, as well as sperm quality. It has been suggested that the presence, position and retardance of the optically birefringent meiotic spindle (MS) are related to oocyte developmental competence, affecting the quality of fertilization and embryo development. Artificial intelligence (AI) will be applied to establish the complex oocyte quality, embryo ploidy and pregnancy success probability from the sequence of data, starting with the recording of the meiotic spindle in polarized light, through paternal factors up to the time lapse recording of early embryo development.

Synergic approaches will be used to increase the quality of embryos for implantation: image analysis and machine learning techniques will be applied to the oocyte microscopic images to perform the MS analysis fully automatically and to determine whether some other aspects of the oocyte appearance might correlate with the optimal timing and fertilization and pregnancy success, or genetic defects. An automatic method of embryo evaluation based on time-lapse videos after ICSI and MS imaging plus other scalar parameters (extracted features can be used as inputs for the downstream tasks, e.g. features extracted from oocytes and sperm can serve as additional inputs to the embryo classifier) will be used. This strategy should reduce the cost of fertility treatment thanks to increased efficiency in choosing the most promising candidates and reducing the need for costly laboratory analyses.

The analysis will be performed in cooperation with Czech Technical University and Institute of Physics Academy of Sciences of the Czech Republic who will create a software tool capable of predicting the probability of pregnancy and embryo ploidy status from oocyte images plus time-lapse video of a developing embryo after ICSI. It will be determined whether some other aspects of the oocyte appearance correlate with the fertilization and pregnancy success, or genetic defects.

Time lapse sequences of embryonic development and oocyte images will be acquired from VFN and from cooperating IVF centres (Gynem, s.r.o., Repromeda, s.r.o.). The sequences will be stored and paired with outcome (ploidy status, pregnancy) and also with previously acquired oocyte images. BIOCEV (Academy of sciences of the Czech Republic) will evaluate sperm parameters with respect to oocyte fertilization rate and early embryonic development.

Study Type

Observational

Enrollment (Estimated)

1000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

  • Name: Jaromir Masata, MD
  • Phone Number: +420603444662
  • Email: masata@volny.cz

Study Locations

  • Czechia
      • Prague, Czechia, 128 08
        • Recruiting
        • General University Hospital in Prague
        • Contact:
      • Prague, Czechia
        • Recruiting
        • Czech Technical University in Prague
        • Contact:
      • Prague, Czechia
        • Recruiting
        • Institute of Physics AS CR
        • Contact:
          • Irena Kratochvilova, Prof.
          • Phone Number: +420266052524
          • Email: krat@fzu.cz
      • Vestec, Czechia

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Infertility couples, where ICSI and PDT are indicated

Description

Inclusion Criteria:

  • Intracytoplasmatic Sperm Injection
  • Preimplantation genetic testing
  • Time lapse embryo record
  • Singned informed consent

Exclusion Criteria:

  • Gynecological diseases
  • Genetical diseases of parents

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The relative number of embryos whose ploidy was correctly predicted by AI
Time Frame: 1 hour
Using an AI based non-invasive method of selecting a high-quality and genetically healthy embryos will undoubtably improve clinical and diagnostic practice and reduce costs in the field of infertility treatment. Both the segmentation and classification training will be based on expert annotations. The approach should lead to a classification accuracy at least 70%.
1 hour

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Charles University, Czech Republic

Collaborators

General University Hospital, Prague
Czech Academy of Sciences
Czech Technical University in Prague

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 5, 2024

Primary Completion (Estimated)

December 31, 2027

Study Completion (Estimated)

March 31, 2028

Study Registration Dates

First Submitted

August 1, 2024

First Submitted That Met QC Criteria

August 1, 2024

First Posted (Actual)

August 6, 2024

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

January 17, 2025

Last Verified

January 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • NW24-08-00048

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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