After a woman gives birth, sometimes a specialist will conduct a placenta exam to analyze the organ for anything that might indicate health problems with the new baby or future pregnancies. The placenta is vital because it delivers blood, oxygen, and other vital substances to the baby via the umbilical cord.

In the US, a placenta exam is time-consuming, expensive, and requires an expert eye, which might explain why the analysis only happens after about 20 percent of births. Thanks to a new AI algorithm, more women could soon benefit from a placenta exam.

During one of these exams, doctors typically look for a certain type of blood vessel damage called decidual vasculopathy (DV). Just one single deceased blood cell could be a sign that a new mom is at risk of developing a potentially deadly complication called preeclampsia during a future pregnancy, something that affects 2 to 8 percent of pregnancies.

Now researchers from Carnegie Mellon University (CMU) and the University of Pittsburgh Medical Center have developed an algorithm capable of spotting signs of DV in images of placenta tissue. Each of these images, which can be generated using commercially available scanners, depicts a thin slice of a placenta with hundreds of blood vessels.

The researchers started by training their algorithm to identify each vessel in an image. They then taught it to decide whether each individual vessel is healthy or damaged, taking into account factors about the pregnancy (the mother’s health, the baby’s birth weight, etc.) before making its decision.

If the algorithm decides that even one blood vessel is damaged, it flags the image, letting a doctor know it deserves a closer look. During testing, the algorithm correctly identified damaged vessels 94% of the time and healthy vessels 96% of the time, according to the researchers’ paper, published in the American Journal of Pathology.