Nonlinear microscopy for egg/embryo viability

Harvard University, Cambridge MA

Investigators

Abstract

In vitro fertilization (IVF) clinics perform the diagnostic, surgical and biological procedures necessary for in vitro fertilization cycles, as well as egg freezing procedures and egg and embryo banking. IVF clinics are highly competitive with one another because their success rates are published, and patients are willing to travel to clinics with higher success rates. Especially at high volume clinics, they are interested in innovations that will increase their profitability by driving patient volume through increased success rates. This team aims to create a commercial device that unearths new sources of information of egg/embryo quality that can be incorporated into an algorithm to predict the chance of IVF success. This team is proposing the creation of an algorithm that can assess the quality of an egg or embryo by looking at metabolic and subcellular features of that egg or embryo and correlating those features to ultimate potential to lead to a live birth. The embryologist can use this algorithm to choose the embryo for implantation that is most likely to lead to birth, which should increase success. Clinicians can also use the algorithm on a patient?s eggs, to give the patient a more accurate assessment of her individual fertility potential. Quantifying metabolism is the core piece that will be used to create the algorithm.