Multi-modal AI for opportunistic screening, staging and progression risk stratification of steatotic liver disease

The global rise in steatotic liver disease poses a significant public health challenge. While non-contrast computed tomography scans hold promise for opportunistic detection of steatotic liver disease, their potential for staging and risk assessment remains underexplored. Here we present a multimodal AI model trained on a large dataset, comprising of (n=968) histopathologically and (n=1103) radiologically confirmed cases, validated against both histology (n=660) and MRI-PDFF (n=375) gold standards, demonstrating high accuracy in detecting mild to severe steatosis (AUC: 0.904–0.929) and clinically significant fibrosis (AUC: 0.824–0.888). Furthermore, integrating the model into the standard clinical pathway improves primary risk screening in a retrospective patient cohort (n=1192), identifying 36% more patients at risk of fibrosis progression. Using Cox proportional hazard model, we observe that the intermediate-high risk patients identified by the optimized clinical pathway exhibits a significantly higher incidence of cirrhosis (hazard ratio: 5.54: 2.69–11.42), showcasing the model’s potential for early detection and management of steatotic liver disease. This study presents MAOSS, a multimodal AI model that repurposes non-contrast CT scans and leverages clinical features to detect and stage liver steatosis and fibrosis. Here the authors show MAOSS accurately stratifies cirrhosis progression risk when embedded into the standard clinical workflow, enabling scalable, opportunistic screening for early intervention of steatotic liver disease.