Can AI Lead to Pregnancy?

Sometimes yes, if a couple struggling to conceive turn to machine learning to pick the right embryo for implantation

2 min read
embryos
Colorful, time-lapsed heat maps use AI to highlight the features of embryos that could be more likely to successfully implant and develop into a pregnancy.
Image: AiVF

Artificial intelligence in healthcare is often a story of percentages. One 2017 study predicted AI could broadly improve patient outcomes by 30 to 40 percent. Which makes a manifold improvement in results particularly noteworthy. 

In this case, according to one Israeli machine learning startup, AI has the potential to boost the success rate of in vitro fertilization (IVF) by as much as 3x compared to traditional methods. In other words, at least according to these results, couples struggling to conceive that use the right AI system could be multiple times more likely to get pregnant.

The Centers for Disease Control and Prevention defines assisted reproductive technology (ART) as the process of removing eggs from a woman’s ovaries, fertilizing it with sperm and then implanting it back in the body.

The overall success rate of traditional ART is less than 30%, according to a recent study in the journal Acta Informatica Medica

But, says Daniella Gilboa, CEO of Tel Aviv, Israel-based AiVF—which provides an automated framework for fertility and IVF treatment—help may be on the way. (However, she also cautions against simply multiplying 3x with the 30% traditional ART success rate quoted above. “Since pregnancy is very much dependent on age and other factors, simple multiplication is not the way to compare the two methods,” Gilboa says.)

In the U.S. alone, 7.3 million women are battling infertility, according to a 2020 report from the American Society for Reproductive Medicine. In the U.S., 2.7 million IVF cycles are performed each year. 

AiVF is using ML and computer vision technology to allow embryologists to discover which embryos have the most potential for success during intrauterine implantation. AiVF is working with eight facilities in clinical trials around the world, including in Israel, Europe and the United States. It plans to launch commercially in 2021.

Ron Maor, head of algorithm research at AiVF, says that AiVF has built its own “bespoke” layer on top of various off-the-shelf AI, ML and deep learning applications. These tools “handle the specific and often unusual aspects of embryo images, which are very different from most AI tasks,” Maor says. 

AiVF’s ML technique involves creating time-lapse videos of developing embryos in an incubator. Over five days, the video shows the milestones of embryo development. Gilboa explains that previous methods yielded just one microscope image per day of the embryo compared with computer vision’s greater image-capturing success.

“By analyzing the video, you could dig out so many milestones and so many features the human eye cannot even detect,” Gilboa says. “Basically you train an algorithm on successful embryos, and you teach the algorithm what are successful embryos.”  

Likely only one embryo out of 10 can be implanted in the uterus. Once a physician implants the embryo, the embryologist will know within 14 days whether the patient is pregnant, Gilboa says. 

“As an embryologist I look at embryos, and I understand what happens to them,” Gilboa says. “If I learn on maybe thousands of embryos, the algorithm would learn on millions of embryos.”

As AiVF’s initial results suggest, computer vision and ML could potentially drive IVF’s prices down—in turn making it less expensive and burdensome for a woman to become pregnant. 

“Once you have a digital embryologist, then you could set up clinics much easier,” Gilboa says. “Or each clinic could be much more scalable. So many more people could enjoy IVF and achieve their dream of having a child.”

The Conversation (0)

This CAD Program Can Design New Organisms

Genetic engineers have a powerful new tool to write and edit DNA code

11 min read
A photo showing machinery in a lab

Foundries such as the Edinburgh Genome Foundry assemble fragments of synthetic DNA and send them to labs for testing in cells.

Edinburgh Genome Foundry, University of Edinburgh

In the next decade, medical science may finally advance cures for some of the most complex diseases that plague humanity. Many diseases are caused by mutations in the human genome, which can either be inherited from our parents (such as in cystic fibrosis), or acquired during life, such as most types of cancer. For some of these conditions, medical researchers have identified the exact mutations that lead to disease; but in many more, they're still seeking answers. And without understanding the cause of a problem, it's pretty tough to find a cure.

We believe that a key enabling technology in this quest is a computer-aided design (CAD) program for genome editing, which our organization is launching this week at the Genome Project-write (GP-write) conference.

With this CAD program, medical researchers will be able to quickly design hundreds of different genomes with any combination of mutations and send the genetic code to a company that manufactures strings of DNA. Those fragments of synthesized DNA can then be sent to a foundry for assembly, and finally to a lab where the designed genomes can be tested in cells. Based on how the cells grow, researchers can use the CAD program to iterate with a new batch of redesigned genomes, sharing data for collaborative efforts. Enabling fast redesign of thousands of variants can only be achieved through automation; at that scale, researchers just might identify the combinations of mutations that are causing genetic diseases. This is the first critical R&D step toward finding cures.

Keep Reading ↓ Show less