S126 - Data-Driven Prediction of Embryo Implantation Probability Using IVF
David H. Silver, Martin Feder, Yael Gold-Zamir, Avital L. Polsky, Shahar
Rosentraub, Efrat Shachor, Adi Weinberger [Technion CS], Pavlo Mazur, Valery D.
Zukin, Alex M. Bronstein [Technion CS].
The process of fertilizing a human egg outside the body in order to help those
suffering from infertility to conceive is known as in vitro fertilization (IVF).
Despite being the most effective method of assisted reproductive technology
(ART), the average success rate of IVF is a mere 20-40%. One step that is
critical to the success of the procedure is selecting which embryo to transfer
to the patient, a process typically conducted manually and without any
universally accepted and standardized criteria. In this paper we describe a
novel data-driven system trained to directly predict embryo implantation
probability from embryogenesis time-lapse imaging videos. Using retrospectively
collected videos from 272 embryos, we demonstrate that, when compared to an
external panel of embryologists, our algorithm results in a 12% increase of
positive predictive value and a 29% increase of negative predictive value.