Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology

Embryo assessment and selection is a critical step in an In-vitro fertilization (IVF) procedure. Current embryo assessment approaches such as manual microscopic analysis done by embryologists or semi-automated time-lapse imaging systems are highly subjective, time-consuming, or expensive. Availability of cost-effective and easy-to-use hardware and software for embryo image data acquisition and analysis can significantly empower embryologists towards more efficient clinical decisions both in resource-limited and resource-rich settings. Here, we report the development of two inexpensive (90% accuracy.