Multi-modal AI for opportunistic screening, staging and progression risk stratification of steatotic liver disease.
Authors
Affiliations (16)
Affiliations (16)
- DAMO Academy, Alibaba Group, Hangzhou, China.
- Hupan Laboratory, Hangzhou, China.
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University, Hangzhou, China.
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, USA.
- DAMO Academy, Alibaba Group, Washington, DC, USA.
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China.
- DAMO Academy, Alibaba Group, Hangzhou, China. [email protected].
- Hupan Laboratory, Hangzhou, China. [email protected].
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China. [email protected].
- Department of Radiology, Mayo Clinic, Rochester, MN, USA. [email protected].
- Department of Infectious Disease, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China. [email protected].
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China. [email protected].
- Hupan Laboratory, Hangzhou, China. [email protected].
- DAMO Academy, Alibaba Group, Washington, DC, USA. [email protected].
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China. [email protected].
Abstract
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.