Abstract
After a child is born, the examination of the placenta by a pathologist for abnormalities, such as infection or maternal vascular malperfusion, can provide important information about the immediate and long-term health of the infant. Detection of the pathologic placental blood vessel lesion decidual vasculopathy (DV) has been shown to predict adverse pregnancy outcomes, such as preeclampsia, which can lead to mother and neonatal morbidity in subsequent pregnancies. However, because of the high volume of deliveries at large hospitals and limited resources, currently a large proportion of delivered placentas are discarded without inspection. Furthermore, the correct diagnosis of DV often requires the expertise of an experienced perinatal pathologist. We introduce a hierarchical machine learning approach for the automated detection and classification of DV lesions in digitized placenta slides, along with a method of coupling learned image features with patient metadata to predict the presence of DV. Ultimately, the approach will allow many more placentas to be screened in a more standardized manner, providing feedback about which cases would benefit most from more in-depth pathologic inspection. Such computer-assisted examination of human placentas will enable real-time adjustment to infant and maternal care and possible chemoprevention (eg, aspirin therapy) to prevent preeclampsia, a disease that affects 2% to 8% of pregnancies worldwide, in women identified to be at risk with future pregnancies.