Data-Driven Prediction of Embryo Implantation Probability Using IVF Time-lapse Imaging

David H. Silver, Martin Feder, Yael Gold-Zamir, Avital L. Polsky, Shahar Rosentraub, Efrat Shachor, Adi Weinberger, Pavlo Mazur, Valery D. Zukin, Alex M. Bronstein

Track: short paper

TL;DR: A novel data-driven system trained to directly predict embryo implantation probability from embryogenesis time-lapse imaging videos

Keywords: Deep Learning, In Vitro Fertilization, Video Classification

Abstract: 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.

Paper Type: both

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