Key result
SCOUT-Echo-AI evaluates opportunistic AI screening on routine echocardiograms to improve liver disease detection.
Why the study?
Steatotic liver disease is highly prevalent but often undiagnosed in cardiovascular patients, and the clinical utility of AI-enabled opportunistic liver disease screening using routine echocardiography is unknown.
Does AI-enabled opportunistic screening using the EchoNet-Liver algorithm on routine echocardiograms improve the detection rates of steatotic and fibrotic liver disease compared to usual care?
Does AI-enabled opportunistic screening using the EchoNet-Liver algorithm on routine echocardiograms improve the detection rates of steatotic and fibrotic liver disease compared to usual care?
The SCOUT-Echo-AI study will evaluate whether an AI algorithm can opportunistically and accurately screen for undiagnosed liver disease using routinely obtained but clinically unused subcostal echocardiographic views.
BACKGROUND: Steatotic liver disease (SLD) is highly prevalent among individuals with cardiovascular disease, but often remains undiagnosed until advanced sequelae such as fibrotic liver disease and hepatocellular carcinoma develop. Transthoracic echocardiography routinely captures incidental subcostal views of the liver that are not used clinically. The EchoNet-Liver algorithm uses artificial intelligence (AI) to predict the presence of liver disease based on routine clinical echocardiograms. Prospective evaluation in real-world practice is needed to understand the clinical utility and impact of AI-enabled opportunistic liver disease screening. METHODS: The SCOUT-Echo-AI study is a single-arm, prospective, multicenter, open-label pragmatic interventional trial that will be conducted across six diverse U.S. healthcare systems (Kaiser Permanente Northern California, Stanford Healthcare, Cedars-Sinai Medical Center, University of California Los Angeles, Northwestern Medicine, and Massachusetts General Brigham). Routine echocardiograms will be analyzed by the EchoNet-Liver AI algorithm with an estimated enrollment of ∼3,000 patients. Treating clinicians or participants themselves will be notified of potential undiagnosed liver disease with site-specific workflows in place to ensure routine clinical care for definitive case confirmation. Primary outcomes will be SLD and fibrotic liver disease detection rates and positive predictive values within 12 months of AI identification. Pre-specified secondary outcomes will assess diagnostic accuracy against other gold-standard modalities, treatment initiation, time to diagnosis, and impact on other clinical events. Outcomes will be compared against historical usual-care and contemporary below-threshold cohorts. CONCLUSION: SCOUT-Echo-AI is a prospective multicenter study evaluating the real-world impact of AI-enabled opportunistic liver disease screening using routine echocardiography.
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Eusuff et al. (2026) studied Steatotic liver disease (n=3,000). EchoNet-Liver AI algorithm vs. Historical usual-care and contemporary below-threshold cohorts was evaluated on Steatotic liver disease (SLD) and fibrotic liver disease detection rates and positive predictive values within 12 months of AI identification. The SCOUT-Echo-AI trial will evaluate whether opportunistic screening using the EchoNet-Liver AI algorithm on routine echocardiograms improves the detection of steatotic and fibrotic liver disease.
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