Morphological oocyte selection by Artificial Intelligence

Oocyte morphology and efficiency of the oocyte donor are significant factors for successful egg banking programs. Semantic segmentation of donor oocyte images is achieved with deep neural networks, and systems get information regarding the morphological specification of each oocyte and the efficiency of the egg donor from previous cycles.

Ovogene bank uses Artificial intelligence (AI) for morphological donor oocyte selection from the stage after the denudation process. Senior embryologists select oocytes of high morphological quality, and then an active time-lapse camera captures live images of a group of oocytes. After the image recognition semantic, the segmentation system launches, and morphological calculation starts for different parameters.

The morphological calculation can be managed for zona pellucida thickness, perivitelline space diameter, oocyte inner and outer diameter, polar body number and size, cytoplasmic granulation and spindle localization. After the quick calculation, the embryologist can get information about available oocytes for egg banking purposes. In the last stage, all this information gets complete with the medical efficiency of an egg donor, and we decide which oocyte we are going to vitrify for egg banking.

AI-based selection will give safety control regarding morphological algorithms for egg banks and customer clinics. Ovogene always follows new technical strategies that can increase egg banking program efficiency for individual patients and clinics. AI-based selection will create a strong efficiency report when customer clinics send feedback regarding the result of the donor oocyte thawing program.

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