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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence in Healthcare
  • Embryology

Background:

  • Embryo selection in in vitro fertilization (IVF) seeks to identify embryos with the highest reproductive potential.
  • Preimplantation genetic testing for aneuploidy (PGT-A) is the standard but is invasive and not always feasible.
  • Deep learning (DL) models, like the iDA score, offer non-invasive embryo assessment alternatives.

Purpose of the Study:

  • To critically evaluate the relationship between iDAScore (versions 1.0 and 2.0), embryo euploidy, and clinical outcomes (live birth, miscarriage).
  • To assess the predictive performance of iDAScore compared to chromosomal status and reproductive results.

Main Methods:

  • A narrative review of studies published from January 2020 to May 2025.
  • Searches conducted on PubMed and Google Scholar using keywords: 'iDAScore,' 'deep learning,' 'euploidy,' 'live birth.'
  • Inclusion of English-language, full-text studies assessing iDAScore's predictive accuracy for chromosomal status or reproductive outcomes.

Main Results:

  • Six retrospective studies were included, all showing a significant association between higher iDAScore values and embryo euploidy.
  • Area Under the Curve (AUC) values for euploidy prediction ranged from 0.60 to 0.68.
  • iDAScore positively correlated with live birth rates and negatively with miscarriage rates, with moderate accuracy in euploid cohorts.

Conclusions:

  • iDAScore shows potential as an adjunct tool for embryo assessment in IVF.
  • It may assist in prioritizing embryos for transfer when PGT-A is not feasible.
  • Further large-scale prospective studies are needed to confirm its clinical utility.