Researchers reported a machine-learning approach that combines automated whole-brain MRI segmentation with APACHE II and SOFA scores to flag sepsis-associated encephalopathy with an AUC of 0.92. The work, built around XGBoost classification, targets a critical clinical gap: identifying brain complications early enough to influence immediate care pathways.