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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Yuan Gao,
  • Chunli Li,
  • Wanxing Chang,
  • Bai Du,
  • Xianghua Ye,
  • Yee Hui Yeo,
  • Yingda Xia,
  • Heng Guo,
  • Xiaoming Zhang,
  • Wei Liu,
  • Ruobing Bai,
  • Beibei Li,
  • Yang Hong,
  • Jiawen Yao,
  • Le Lu,
  • Kai Cao,
  • Ke Yan,
  • Jun Chen,
  • Jie Li,
  • Yang Hou,
  • Ling Zhang,
  • Yu Shi

摘要

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.