It is critical for doctors to examine a placenta after the birth of a baby to determine a mothers health risks for any future pregnancies. The process is often a long and time-consuming, and despite its important to examine, may placentas go unchecked. Sometimes, a woman may have blood vessel lesions called decidual vasculopathy (DV) that goes unchecked and places a future pregnancy at risk for preeclampsia.
To address such a challenge, researchers at Carnegie Mellon University (CMU) and the University of Pittsburgh Medical Center (UPMC) developed a machine learning approach to examine placental slides and decrease the likelihood of failed future pregnancies.
"Pathologists train for years to be able to find disease in these images, but there are so many pregnancies going through the hospital system that they don't have time to inspect every placenta," said Daniel Clymer, PhD, alumnus, Department of Mechanical Engineering, CMU, Pittsburgh, PA, USA. "Our algorithm helps pathologists know which images they should focus on by scanning an image, locating blood vessels, and finding patterns of the blood vessels that identify DV."
Findings were reported in the American Journal of Pathology.
"This algorithm isn't going to replace a pathologist anytime soon," Dr. Clymer explained. "The goal here is that this type of algorithm might be able to help speed up the process by flagging regions of the image where the pathologist should take a closer look."
Learn more about placental complications:
Source: Science Daily
