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Human Oocyte Morphology and Outcomes of Infertility Treatment: a Systematic Review.

Dmitry NikiforovMarie Louise GrøndahlJulius HreinssonClaus Yding Andersen
Published in: Reproductive sciences (Thousand Oaks, Calif.) (2021)
Oocyte morphology assessment is easy to implement in any laboratory with possible quality grading prior to fertilization. At present, comprehensive oocyte morphology scoring is not performed as a routine procedure. However, it may augment chances for successful treatment outcomes if a correlation with certain dysmorphisms can be proven. In order to determine a correlation between oocyte morphology and treatment outcome, we performed a systematic search in PubMed and Cochrane Controlled Trials Register following PRISMA guidelines. A total of 52 articles out of 6,755 search results met the inclusion criteria. Dark colour of the cytoplasm (observed with an incidence rate of 7%), homogeneous granularity of the cytoplasm (19%) and ovoid shape of oocytes (7%) appeared to have no influence on treatment outcome. Abnormalities such as refractile bodies (10%), fragmented first polar body (37%), dark zona pellucida (9%), enlarged perivitelline space (18%) and debris in it (21%) are likely to affect the treatment outcome to some extent. Finally, cytoplasmic vacuoles (4%), centrally located cytoplasmic granularity (12%) and clusters of smooth endoplasmic reticulum (4%) negatively impact infertility treatment outcomes. Nonetheless, morphological assessment is informative rather than predictive. Adding oocyte morphology to the artificial intelligence (AI)-driven selection process may improve the precision of the algorithms. Oocyte morphology assessment can be especially useful in oocyte donation cycles, during oocyte freezing for fertility preservation and finally, objective oocyte scoring can be important in cases of very poor treatment outcome as a tool for explanation of results to the patient.
Keyphrases
  • artificial intelligence
  • machine learning
  • deep learning
  • big data
  • clinical practice
  • type diabetes
  • adipose tissue
  • young adults
  • polycystic ovary syndrome
  • randomized controlled trial
  • insulin resistance