After a baby is born, doctors sometimes examine the placenta—the organ that links the mother to the baby—for features that indicate health risks in any future pregnancies.
Unfortunately, this is a time-consuming process that must be done by a specialist, so most placentas go unexamined after birth. A team of researchers from Carnegie Mellon University (CMU) and the University of Pittsburgh Medical Center (UPMC) developed a machine learning approach to examine placenta slides so more women can be informed of their health risks.
The Phys.org website cited researcher Daniel Clymer saying "Pathologists train for years to be able to find disease in the placenta, but there are so many pregnancies going through the hospital system that they don't have time to inspect every placenta. Our algorithm helps pathologists examine the slides after scanning and storing the images on a computer, so they can evaluate the placenta's health."
But given that examining and evaluating the complete image of a placenta is a difficult task, the new technique uses software that locates blood vessels, and then examines each one of these vessels to determine whether it is healthy or not.
The software can also evaluate the mother's health after delivery, and help determine whether she suffers from diseases or health problems.
This technology can decrease healthcare costs, allowing a majority of mothers and infants to have access to a microscopic placenta examination. "This algorithm isn't going to replace a pathologist anytime soon, but it might be able to help speed up the process by flagging regions of the image where the pathologist should take a closer look," Clymer said.
