Feb 12 2026

A novel artificial intelligence (AI) model accurately detected the presence of placenta accreta spectrum (PAS), a dangerous pregnancy condition that often goes undetected with current screening methods, according to new research presented today at the Society for Maternal-Fetal Medicine (SMFM) 2026 Pregnancy Meeting™.  PAS is a leading cause of maternal mortality and morbidity, but only half of all cases are diagnosed during pregnancy, researchers say. 

PAS is a life-threatening pregnancy complication in which the placenta abnormally attaches to the uterine wall, often associated with prior uterine surgical procedures such as cesarean delivery. The incidence of PAS is increasing in the U.S. An underdiagnosed condition before delivery, placenta accreta can result in massive maternal hemorrhage, multisystem organ failure, and death. Pregnancies at high risk for PAS are typically screened based on risk factors and ultrasounds to help identify and prepare for the problem before delivery occurs, but many factors can lead to inconclusive findings or a misdiagnosis. 

“Our team is very excited about the potential clinical implications of this model for accurate and timely diagnosis of PAS,” said researcher Alexandra L. Hammerquist, MD, a maternal-fetal medicine fellow at Baylor College of Medicine in Houston, TX. “We are hopeful that its use as a screening tool will help decrease PAS-related maternal morbidity and mortality.” 

Using an innovative AI program, researchers from Baylor College of Medicine retrospectively reviewed the 2D obstetric ultrasound images from 113 patients at risk for PAS who gave birth at Texas Children’s Hospital between 2018 – 2025. The mean gestational age at the time of maternal ultrasound was 30.89 +3.67 weeks. 

Based on a retrospective review of the 2D ultrasound images from the 113 patients, the researchers found that their AI model accurately detected the presence of all cases of PAS. There were two (2) false positives but no false negative reports of placenta accreta. 

Source:

http://Society for Maternal-Fetal Medicine