(IVF revolutionized fertility. Will these new methods do the same?)
Keeping track of the healthiest embryos
Once the sperm meets the egg, the embryos sit in an incubator for five days. At that point, the embryo becomes a blastocyst and is graded based on the quality of its inner and outer cells. Both components receive a quality grade of A, B, or C. The two letters are then put together—so a blastocyst might be AA if it has the highest quality inner and outer cells or BC if it has mid-quality inner and low-quality outer cells.
To grade a blastocyst, an embryologist will look at time-lapse videos or single images of the embryos in addition to looking at them under a microscope. This process is subjective, time-consuming, and hasn’t changed much since the first IVF birth in 1978. But AI could change that: a 2024 clinical study published in Nature Medicine found that an AI system trained on data from over 115,000 embryos was nearly as good as humans at grading embryos.
After embryos are graded, genetic testing is often done to determine if an embryo is normal or abnormal. Usually, an embryologist hand tests each embryo to ensure it has the correct number of chromosomes. AI can help here, too.
In 2023, Nikica Zaninovic, Associate Professor of Embryology at Weill Cornell Medical College, and colleagues published a study in The Lancet that found that an AI algorithm can determine if an embryo has a normal number of chromosomes with about 70 percent accuracy, which is statistically significant. While traditional biopsy methods are around 90 percent accurate in ideal conditions, they can be less so in more difficult cases—for example, with mosaic embryos that contain a mix of normal and abnormal cells.
Researchers trained the AI system by feeding it pictures of both normal and abnormal embryos. From those images, the AI learned to predict whether embryos not included in the original dataset were normal or not. Since AI is evaluating images rather than the embryo itself, it’s not as invasive as the traditional biopsy, reducing the risk of damage to the fragile embryo.
The next step, Zaninovic says, is to add layers to the algorithm, so that it can help earlier in the process, like identifying unhealthy egg cells and predicting how many eggs a woman needs to ensure she freezes enough to have a successful pregnancy.
It also may help with the steps after grading and genetic testing, like assessing the thickness of the uterine lining as well as uterine receptivity after the embryo has been transferred. “Even after [preimplantation genetic testing], you still have miscarriages about 10 to 15 percent of the time,” Zaninovic says. “And that might not be related to the embryo itself, but it could be related to the overall patient wellbeing.”
Another of the challenges AI can help overcome has to do with avoiding potential mix-ups, including an embryo being mislabeled and implanted in a different patient. Embryos are so small—just 0.1 to 0.15 mm in diameter—that mix-ups have happened, though they are rare.
“If an embryo were accidentally moved to the wrong location within a dish, traditional systems would not catch it,” says Charles Bormann, director of embryology at the Massachusetts General Fertility Center. “AI systems are designed to track embryos throughout development and confirm identity at every step, ensuring that each embryo remains correctly labeled and matched to the right patient.”
