Development of an automated two pronuclei detection system on time‐lapse embryo images using deep learning techniques

PURPOSE: To establish an automated pronuclei determination system by analysis using deep learning technology which is able to effectively learn with limited amount of supervised data. METHODS: An algorithm was developed by explicitly incorporating human observation where the outline around pronuclei is being observed in determining the number of pronuclei. Supervised data were selected from the time‐lapse images of 300 pronuclear stage embryos per class (total 900 embryos) clearly classified by embryologists as 0PN, 1PN, and 2PN. One‐hundred embryos per class (a total of 300 embryos) were used for verification data. The verification data were evaluated for the performance of detection in the number of pronuclei by regarding the results consistent with the judgment of the embryologists as correct answers. RESULTS: The sensitivity rates of 0PN, 1PN, and 2PN were 99%, 82%, and 99%, respectively, and the overlapping 2PN being difficult to determine by microscopic observation alone could also be appropriately assessed. CONCLUSIONS: This study enabled the establishment of the automated pronuclei determination system with the precision almost equivalent to highly skilled embryologists.