Abstract
PROBLEM: The placental membranes are a key barrier to fetal and uterine infection. Inflammation of the membranes, diagnosed as maternal inflammatory response (MIR) or alternatively as acute chorioamnionitis, is associated with adverse maternal-fetal outcomes. MIR is staged 1-3, with higher stages indicating more hazardous inflammation. However, the diagnosis relies upon subjective evaluation and has not been deeply characterized. The goal of this work is to develop a cell classifier for eight placental membrane cells and quantitatively characterize MIR1-2. METHOD OF STUDY: Hematoxylin and eosin (H&E)-stained placental membrane slides were digitized. A convolutional neural network was trained on a dataset of hand-annotated and machine learning-identified cells. Overall cell class-level metrics were calculated. The model was applied to 20 control, 20 MIR1, and 23 MIR2 placental membrane cases. MIR cell composition and neutrophil distribution were assessed via density and Ripley's cross K-function. Clinical data were compared to neutrophil density and distribution. RESULTS: The classification model achieved a test-set accuracy of 0.845, with high precision and recall for amniocytes, decidual cells, endothelial cells, and trophoblasts. Using this model to classify 53 073 cells from healthy and MIR1-2 placental membranes, we found that (1) MIR1-2 have higher neutrophil density and fewer decidual cells and trophoblasts, (2) Neutrophils colocalize heavily around decidual cells in healthy placental membranes and around trophoblasts in MIR1, (3) Neutrophil density impacts distribution in MIR, and (4) Neutrophil metrics correlate with features of clinical chorioamnionitis. CONCLUSIONS: This paper introduces cell classification into the placental membranes and quantifies cell composition and neutrophil spatial distributions in MIR.