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

Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-49
SponsorCharles University, Czech Republic

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Intracytoplasmatic Sperm Injection

Preimplantation genetic testing

Time lapse embryo record

Singned informed consent

Disqualifiers

Gynecological diseases

Genetical diseases of parents

Trial population

Infertility couples, where ICSI and PDT are indicated

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • arteficial inteligence evaluation of oocyte and embryo development

    Procedure/Surgery

    apply AI to find out the complex oocyte quality, embryo development, embryo ploidy and pregnancy success probability from the sequence of the data starting from the recording of the meiotic spindle in polarized light, through paternal factors up to the time lapse record of early embryo development.

Treatment groups

No treatment groups listed

Trial outcomes

Primary outcomes

1

The relative number of embryos whose ploidy was correctly predicted by AI

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%.

Time frame
1 hour

Secondary outcomes

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Charles University, Czech Republic

Lead sponsor

Czech Academy of Sciences

Collaborator

Czech Technical University in Prague

Collaborator

General University Hospital, Prague

Collaborator