ArticleThe Journal of infectious diseases2026
Machine Learning-Enabled Quantification of Hepatocellular Necrosis in the Liver After Lethal Marburg and Ebola Virus Exposures.
Article in The Journal of infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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