Harvard College

Ichilov Tech Ltd.

Brian Leahy

Hanspeter Pfister

Daniel Needleman

Dalit Ben Yosef

Yu Yang

Won-Dong Jang

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.

Growing community of inventors

Cambridge, MA, United States of America

Brian Leahy