Machine Learning-Enabled Quantification of Hepatocellular Necrosis in the Liver After Lethal Marburg and Ebola Virus Exposures.

The liver is a key target of pathogenic Marburg and Ebola viruses (MARV and EBOV, respectively), with injury common in severe filovirid disease, yet high-resolution histopathologic quantification is lacking. We addressed this by deploying deep learning (DL) models for pixel-level quantitative analysis of digitized liver pathology slides in lethal rhesus monkey (RM) models of MARV and 2 EBOV variants, Makona and Kikwit. Our DL model segmented arteries, veins, bile ducts, and hepatic necrosis, achieving interobserver variability in necrosis segmentation comparable to that of 3 pathologists. DL-quantified liver necrosis correlated with exposure virus (f = 6.61, P = .006) and was highest in MARV-exposed RMs (11.0%). While filovirid-exposed RMs showed elevations in aspartate aminotransferase, alanine aminotransferase (ALT), gamma-glutamyl transferase, and alkaline phosphatase, only ALT levels correlated with necrosis severity. Necrosis localization differed-portal tract proximate after MARV exposure and central vein proximate after EBOV exposure. This proof-of-concept work enables future large-scale retrospective "meta-pathologic" analyses.