P–247 Application of deep learning for automated measurement of key morphological features of human zygotes for IVF
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ecent researches have explored the use of DL to extract key morphological features of human embryos.
Existing studies, however, focus either on blastocysts’ morphological measurements (Au et al. 2020) or on embryos’ general developmental stages classification (
ttempted to evaluate zygotes’ morphological components but stopped short of identifying the existence and location of their pronuclei
Samples were collected from one clinic with videos recorded from one time-lapse system which can limit our results’ reproducibility. The accuracy of our DL model is also limited by the small number of embryos that we used.
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