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

Condition(s) studied

Investigational drug(s) / intervention(s)

arteficial inteligence evaluation of oocyte and embryo development: 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.

Study summary

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

Primary outcome measure(s)

• The relative number of embryos whose ploidy was correctly predicted by AI — 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%.

Trial sites (4)

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