If a woman (or non-female-identifying person with a uterus and visions of starting a family) is struggling to conceive and decides to improve their reproductive odds at an IVF clinic, they’ll likely interact with a doctor, a nurse, and a receptionist. They will probably never meet the army of trained embryologists working behind closed lab doors to collect eggs, fertilize them, and develop the embryos bound for implantation.

One of embryologists’ more time-consuming jobs is grading embryos—looking at their morphological features under a microscope and assigning a quality score. Round, even numbers of cells are good. Fractured and fragmented cells, bad. They’ll use that information to decide which embryos to implant first.

It’s more gut than science and not particularly accurate. Newer methods, like pulling off a cell to extract its DNA and test for abnormalities, called preimplantation genetic screening, provide more information. But that tacks on additional costs to an already expensive IVF cycle and requires freezing the embryos until the test results come back. Manual embryo grading may be a crude tool, but it’s noninvasive and easy for most fertility clinics to carry out. Now, scientists say, an algorithm has learned to do all that time-intensive embryo ogling even better than a human.

In new research published today in NPJ Digital Medicine, scientists at Cornell University trained an off-the-shelf Google deep learning algorithm to identify IVF embryos as either good, fair, or poor, based on the likelihood each would successfully implant. This type of AI—the same neural network that identifies faces, animals, and objects in pictures uploaded to Google’s online services—has proven adept in medical settings. It has learned to diagnose diabetic blindness and identify the genetic mutations fueling cancerous tumor growth. IVF clinics could be where it’s headed next.

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“All evaluation of the embryo as it’s done today is subjective,” says Nikica Zaninovic, director of the embryology lab at Weill Cornell Medicine, where the research was conducted. In 2011, the lab installed a time-lapse imaging system inside its incubators, so its technicians could watch (and record) the embryos developing in real time. This gave them something many fertility clinics in the US do not have—videos of more than 10,000 fully anonymized embryos that could each be freeze-framed and fed into a neural network. About two years ago, Zaninovic began Googling to find an AI expert to collaborate with. He found one just across campus in Olivier Elemento, director of Weill Cornell’s Englander Institute for Precision Medicine.

For years, Elemento had been collecting all kinds of medical imaging data—MRIs, mammograms, stained slides of tumor tissue—from any colleague who would give it to him, to develop automated systems to help radiologists and pathologists do their jobs better. He’d never thought to try it with IVF but could immediately see the potential. There’s a lot going on in an embryo that’s invisible to the human eye but might not be to a computer. “It was an opportunity to automate a process that is time-consuming and prone to errors,” he says. “Which is something that’s not really been done before with human embryos.”

To judge how their neural net, nicknamed STORK, stacked up against its human counterparts, they recruited five embryologists from clinics on three continents to grade 394 embryos based on images taken from different labs. The five embryologists reached the same conclusion on only 89 embryos, less than a quarter of the total. So the researchers instituted a majority voting procedure—three out of five embryologists needed to agree to classify an embryo as good, fair, or poor. When STORK looked at the same images, it predicted the embryologist majority voting decision with 95.7 percent accuracy. The most consistent volunteer matched results only 70 percent of the time; the least, 25 percent.