Automated analysis and selection of human embryos

Abstract

Embodiments described herein use deep learning to automate measurement of key morphokinetic embryo features associated with viability and quality, in particular those relevant for clinical in-vitro fertilization (IVF). Systems and methods may, for example, acquire one or more digital images of one or more embryos; select one or more embryos in each digital image; and for each selected embryo, (i) computationally characterize the zona pellucida, detect the degree of fragmentation in the embryo, and for each embryo with a low fragmentation score, computationally classify the embryo's developmental stage based on whether cells constituting the embryo exceed a threshold number (e.g., nine). For an embryo consisting of a single cell, pronuclei may be detected and counted. Based on these measurements, a viability score may be assigned to the embryo.