4.5 Review

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

Journal

REPRODUCTIVE SCIENCES
Volume 29, Issue 10, Pages 2768-2785

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s43032-021-00723-y

Keywords

Human oocyte; Oocyte morphology; Oocyte quality; Oocyte dysmorphisms; Assisted reproduction

Funding

  1. European Union programme Interreg OKS
  2. Capital Region of Denmark
  3. Region Skane of Sweden
  4. Ferring Pharmaceuticals

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Assessment of oocyte morphology can impact treatment outcomes, with certain abnormalities potentially affecting the results. Overall, morphology assessment is informative but not predictive, and combining artificial intelligence with oocyte morphology assessment may improve the accuracy of treatment outcomes.
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.

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