Objective: This study aimed to establish a quantitative and interpretable method for assessing oocyte quality by analyzing cytoplasmic morphology and intensity features using artificial intelligence.
Methods: A total of 695 oocyte images were collected from hormonally stimulated young and aged mice. The cytoplasmic region was manually annotated to exclude polar bodies, and radiomics analysis was performed to extract morphological and intensity-based features.
Results: Clustering with a Gaussian mixture model identified three distinct oocyte subtypes with unique cytoplasmic characteristics. Cluster 2, with the most spherical and compact oocytes, demonstrated the highest blastocyst formation rate (42.9%), followed by clusters 3 (35.3%) and 1 (20.4%). Cluster 2 oocytes also showed the highest mean intensity and lowest variability, suggesting uniform cytoplasmic structure. Notably, some aged oocytes in cluster 2 exhibited developmental potential comparable to that of young mice, indicating that cytoplasmic quality may be a more informative predictor than age alone.
Conclusion: These findings underscore the value of cytoplasmic features as objective indicators of developmental competence. This artificial intelligence-driven approach may improve embryo selection by providing a standardized, non-invasive method for evaluating oocytes, ultimately contributing to enhanced clinical outcomes in assisted reproductive technologies.
Keywords: Artificial intelligence; Embryonic development; Fertilization in vitro; Oocyte quality assessment; Reproductive techniques, assisted.