Artificial intelligence - a novel tool in assisted reproduction technology

The project aims to improve the methods for selecting sperm and embryos and increase the chance of pregnancy and live-born children.

The fertility rates have declined in many industrialized countries during the last decades. This can partly be explained by social and economic conditions, but may also be due to a rise in fertility problems. Between 10 and 15 percent of couples are involuntarily childless.

Over the last decades, there has been a development of assisted reproduction technology, and the use of such treatment, also called in vitro fertilization (IVF), is increasing.

Intracytoplasmic sperm injection (ICSI) is a treatment for couples where the male has reduced semen quality, although sometimes also used when the semen quality is normal. By this method, the sperm is injected directly into the egg cell, whereas in ordinary IVF, sperm and egg are mixed in a dish, and one of the sperm cells fertilizes the egg.

The decision about which fertilized egg, called embryo, should be transferred to the woman is based on the appearance of the embryo and how it develops during the first few days after fertilization. Likewise, the sperm used for ICSI is selected upon examination of some characteristics, like the movement and appearance.

The evaluation of embryo and sperm is performed by embryologists, but there are no clear criteria for prediction of pregnancy. And this subjective assessment may not recognize important information for achieving pregnancy.

Goal

The goal is to improve the methods for embryo and sperm selection and thereby increase the chance of pregnancy and ultimately a live born child. Another advantage may be to reduce the number of treatment cycles and to lower cost per treatment.

Method

Artificial intelligence (AI) methods are more and more accepted as an important tool in medicine and are especially suitable to obtain information from images.

In this project, we develop AI methods to analyze videos of embryo development. We have shown that deep learning models are able to characterize cell division and different stages of the embryo development.

Data from these image analyses will be coupled to reproductive outcomes, like pregnancy and live birth, to elucidate if specific features in the development of the embryo increase the probability for treatment success.

We have also developed neural networks which by analyzing videos can categorize the spermatozoa according to their motility.
Videos of spermatozoa, prior to selection for injection into the oocyte by the ICSI method, are presently being analyzed and will thereafter be related to reproductive outcomes. The results will be used to make a tool for fertility clinics to assist in clinical decisions.