  In conventional clinical in-vitro fertilization practices embryos are
transferred either at the cleavage or blastocyst stages of development.
Cleavage stage transfers, particularly, are beneficial for patients with
relatively poor prognosis and at fertility centers in resource-limited settings
where there is a higher chance of developmental failure in embryos in-vitro.
However, one of the major limitations of embryo selections at the cleavage
stage is the availability of very low number of manually discernable features
to predict developmental outcomes. Although, time-lapse imaging systems have
been proposed as possible solutions, they are cost-prohibitive and require
bulky and expensive hardware, and labor-intensive. Advances in convolutional
neural networks (CNNs) have been utilized to provide accurate classifications
across many medical and non-medical object categories. Here, we report an
automated system for classification and selection of human embryos at the
cleavage stage using a trained CNN combined with a genetic algorithm. The
system selected the cleavage stage embryo at 70 hours post insemination (hpi)
that ultimately developed into top-quality blastocyst at 70 hpi with 64%
accuracy, outperforming the abilities of embryologists in identifying embryos
with the highest developmental potential. Such systems can have a significant
impact on IVF procedures by empowering embryologists for accurate and
consistent embryo assessment in both resource-poor and resource-rich settings.

Tissues and Organs;Computer Vision and Pattern Recognition;Image and Video Processing