AI portal tract detection and characterisation for a regional analysis of steatosis and inflammation in MASLD, MASH, and AIH

Background & AimsAnnotation of liver biopsies, for disease staging is increasingly aided by digital pathology, however existing systems do not quantify inflammation and steatosis within an anatomical framework. We developed an AI system to quantify portal tracts (PT) and disease features and their regional distribution in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)/Metabolic Dysfunction-Associated Steatohepatitis (MASH) and Autoimmune Hepatitis (AIH). MethodsDigitised images of haematoxylin and eosin-stained specimens were pooled from 4 clinical cohorts (n=390: 89 MASLD, 238 MASH, 63 AIH). Portal tracts, regional steatosis, interface hepatitis, portal and lobular inflammation were quantified using a proprietary AI system and scored by expert pathologists. ResultsThe percentage of steatosis was higher in MASH (7.5%) compared to MASLD (3.2%, p<0.0001). Lobular regions had larger steatotic vesicles (260 vs 190mm2, p<0.0001). AI-derived steatosis quantification correlated with manual grading (rs=0.72; p<0.0001). The inflammatory cell number (ICN) was 2-fold higher in AIH compared to MASLD/MASH in interface [390 vs 140; p<0.0001], portal [4600 vs 1500], and lobular [1500 vs 650] regions. Severity of portal inflammation from manual grading correlated well with ICN count at PT (rs=0.71; p<0.0001) but not lobular regions (rs=<0.1). For equivalent grades of portal inflammation, the ICN was up to 3-fold higher in AIH than in MASLD/MASH (rs=0.71; p<0.0001). ConclusionDespite equivalent pathologist portal inflammation grades, the digital burden of inflammation was significantly higher in AIH than MASLD/MASH. This digital system quantifies PT, inflammation, and steatosis, providing powerful decision support for pathologists in AIH diagnosis and MASH staging. LAY SUMMARYThe detection of portal tracts is an important part of liver biopsy sample quality control and histological scoring. Using artificial intelligence, this study demonstrates a system that automatically detects and quantifies portal tracts and surrounding patterns of inflammation and steatosis. AI found that inflammation was in similar regions but was higher in autoimmune hepatitis than in metabolic dysfunction-associated steatohepatitis, despite similar grading from manual scoring. This AI system provides granular information that can aid biopsy grading and provide insights into liver disease progression and diagnosis.