The characteristics of the human pronuclei (PNs), which exist 16-22 h after fertilization, appear to serve as good indicators to evaluate the quality of human oocyte and embryo, and may reflect the status of female and male chromosome composition. Here, a quantitative PN measurement method that is generated by applying expert experience combined with deep learning from large annotated datasets is reported. After mathematic reconstruction of PNs, significant differences are obtained in chromosome-normal rate and chromosomal small errors such as copy number variants by comparing the size of the reconstructive female PN. After integrating the whole procedure of PN dynamics and adjusting for errors that occur during PN identification, the results are robust. Notably, all positive prediction results are obtained from the female propositus population. Thus, the size of female PNs may mirror the internal quality of the chromosomal integrity of the oocyte. Embryos that develop from zygotes with larger female PNs may have a reduced risk of copy number variations.
Keywords: artificial intelligence; expert experience deep learning; mathematical models; pre‐implantation genetic tests; pronuclei identification.
© 2022 The Authors. Advanced Genetics published by Wiley Periodicals LLC.