Machine learning identification of maternal inflammatory response and systemic inflammatory response from placental membrane whole slide images.

INTRODUCTION: The placenta forms a critical barrier to infection through pregnancy, labor and, delivery. Acute placental inflammation in the membranes, diagnosed as Maternal Inflammatory Response (MIR), has short-term, and long-term consequences for offspring health. Digital pathology and machine learning can play an important role in understanding placental inflammation. The goal of this work is to develop machine learning models to predict pathologist-classified MIR stages from based on whole slide images (WSI) and establish early benchmarks. METHODS: We used digitized histopathology whole slide images (WSI), the UNI foundation model and the Multiple Instance Learning (MIL) framework to predict MIR from WSI images. To assess the relationship between local and systemic inflammation, we also attempt prediction of white blood cells count (WBC) and maximum fever temperature (Tmax). To analyze model performance, we built a classifier to identify inflammation in individual patches extracted from whole slide images. RESULTS: We were able to classify MIR with a balanced accuracy of up to 88.4% with a Cohen's Kappa (κ) of up to 0.758. We found that deviations from the pathologist's diagnoses were driven unusually low or high frequencies of patches with inflammation. For WBC and Tmax prediction, we found mild correlation between actual values and those predicted from histopathology WSIs. DISCUSSION: MIR staging is reasonably well predicted using MIL on WSIs. We show a weak but statistically significant correlation between histologic findings and maternal systemic inflammation. These findings could be used to improve reliability of classification and prediction of neonatal outcomes.