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

ABSTRACT: Embryo assessment and selection is a critical step in an in vitro fertilization (IVF) procedure. Current embryo assessment approaches such as manual microscopy 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 (<$100 and <$5) and automated imaging platforms that utilize advances in artificial intelligence (AI) for rapid, reliable, and accurate evaluations of embryo morphological qualities. Using a layered learning a

SUBMITTER: Kanakasabapathy MK 

PROVIDER: S-EPMC6934406 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature