ArticleNature communications2026
Multi-modal AI for opportunistic screening, staging and progression risk stratification of steatotic liver disease.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07613827 (AI-Driven Opportunistic Screening and Risk-Adapted Management of Steatotic Liver Disease), which is not on this map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
AI-Driven Opportunistic Screening and Risk-Adapted Management of Steatotic Liver Disease
Who cites it
8 citing papers in PubMed.
- Analytical Validation and Preliminary Clinical Evaluation of Plasma Carboxylesterase 1 for Ultrasound-Defined Hepatic Steatosis.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Radiomics-Based Prediction of Treatment Response in Non-Small Cell Lung Cancer Using Pre-Treatment CT Imaging Features.Diagnostics (Basel, Switzerland) · 2026Article
- PET Imaging in Vulvar Cancer: A Literature Review of the Current Evidence and Clinical Applications.Cancers · 2026Review
- Toward Explainable Precision Nephrology: Machine Learning-Based Chronic Kidney Disease Prediction.Biomedicines · 2026Article
- A data-driven approach to hepatitis C forecasting using machine learning and epidemiological models.PloS one · 2026Article
- How advances in machine learning drive early detection and risk prediction of early-onset colorectal cancer.Frontiers in oncology · 2026Review
- The role of healthy lifestyle in the association between hepatic fibro-inflammation and steatosis and brain aging-a cross-sectional study.Frontiers in aging neuroscience · 2026Article
Corrections and comments
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Authors and funding
22 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.