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Medical Xpress · September 15, 2026

AI helps pathologists find signs of preeclampsia, advancing diagnosis and treatment

MedicinePublic HealthBiomedical Engineering
THE AI ANGLE
Screening placental blood vessels and calculating explainable cellular morphology scores to flag preeclampsia risk

Researchers at Carnegie Mellon University and UPMC developed an explainable machine learning model to detect decidual vasculopathy, a placental blood vessel disease linked to postpartum preeclampsia. By analyzing the spatial organization of extravillous trophoblast cells and red blood cells, the algorithm generates a morphology separation score to explain its findings and flag high-risk cases for specialist review. This tool addresses a critical public health bottleneck caused by a shortage of perinatal pathologists, which currently leaves fewer than 20% of U.S. placentas screened after delivery.

THE TEACHING ANGLE
Instructors can explore how engineering explainable metrics—such as morphology separation scores—helps resolve the tension between deploying automated screening tools to relieve workforce shortages and maintaining clinical trustworthiness without relying on 'black box' algorithms.

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