Doctors helping couples conceive through in-vitro fertilization typically must screen multiple fertilized embryos to select one embryo for implantation—but the process is fraught with risk and subjectivity. from In 2018 Gilboa and her colleagues Daniel Seidman and Eyal Schiff co-founded AIVF, an Israel-based startup developing decision support tools that use deep learning and computer vision to lower the risk by identifying the most promising embryos for intrauterine implantation.

The company's technology takes the place of old-fashioned visual evaluation of embryos by humans, instead of capturing time-lapse video of embryos from the moment of conception to the fifth day after conception, at multiple focal planes. "It's an obscene amount of data," Gilboa says. "Instead of looking at the embryo once a day under the microscope, we have tons of images to annotate and look for the biological features that we know are correlated with success."

Proprietary machine learning algorithms use the video data, together with patients' health history and genomic data, to predict which embryos have the highest chance of developing into a healthy newborn. In theory, the technology will lower failure rates, decreasing the number of fertility cycles required for conception and therefore lowering the overall cost of IVF treatment.

"Many people don't get to fulfill their dream of having a child, and this is really heartbreaking for me," Gilboa tells Harry. "This is what really drives me as an embryologist to be able to provide a new, next-generation IVF treatment that would be accessible, that wouldn't be so expensive."

Check out the full show notes for this episode and other MoneyBall Medicine episodes on our website. For more on how data is transforming reproductive medicine, listen to Harry's interview with Alan Copperman.

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