Weakly supervised deep learning distinguishes alcohol-associated from metabolic dysfunction-associated...

Distinguishing alcohol-associated steatohepatitis (ASH) from metabolic dysfunction-associated steatohepatitis (MASH) is challenging given the absence of pathognomonic differentiators. We developed and evaluated a weakly supervised deep learning model, as a single-institution proof-of-concept study, to test whether routine hematoxylin and eosin (H&E) whole-slide images (WSIs) of liver biopsies contain sufficient morphological information to predict steatohepatitis etiology at the slide level. A retrospective cohort of 1147 WSIs was assembled (train set: 1007, holdout test set: 140). Models were trained using 5-fold patient-level cross-validation at ×20 and ×40 magnification. Model interpretability was assessed through attention-based clustering with blinded pathologist review of high-attention patches. The ×20 model achieved a mean area under the receiver operating characteristic of 0.86 ± 0.01 on the test set, with balanced accuracy of 0.80. The ×40 model performed comparably. Specificity was high (0.93 at ×20), with an ASH sensitivity of 0.67 at ×20. Fibrosis-stratified showed preserved performance at advanced fibrosis (stage ≥3) with balanced accuracy of 82.5% and ASH sensitivity of 77.4%. Attention-based clustering localized ASH-enriched regions to active injury patterns including Mallory-Denk bodies, neutrophilic inflammation, cholestatic change, and pericellular fibrosis, whereas MASH-enriched regions showed steatosis with lower inflammatory activity. Weakly supervised deep learning applied to routine liver H&E WSIs can discriminate ASH from MASH with performance preserved at advanced fibrosis. The comparable performance of ×20 and ×40 magnification, and the alignment of model attention with established histological features, support the use of routine morphology as a decision-support input in cases with incomplete or conflicting clinical histories, particularly when the etiological distinction has the greatest implications for management.