ArticleNEJM AI2025
Opportunistic Screening of Chronic Liver Disease with Deep-Learning-Enhanced Echocardiography.
Article in NEJM AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Multicenter External Validation of an AI-Based Funduscopic Carotid Atherosclerosis Score and Assessment of Its Association With Coronary Artery Calcification: External Validation Study.JMIR medical informatics · 2026Article
- Multi-task artificial intelligence annotation of echocardiographic images: a retrospective multi-cohort study.medRxiv : the preprint server for health sciences · 2026Article
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- Artificial intelligence prediction of age from echocardiography as a marker for cardiovascular disease.NPJ digital medicine · 2025Article
- Artificial intelligence automation of echocardiographic measurements.medRxiv : the preprint server for health sciences · 2025Article
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9 authors.
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Abstract
backgroundChronic liver disease (CLD) affects more than 1.5 billion adults, most of whom are asymptomatic and undiagnosed. Echocardiography is broadly performed and visualizes the liver, but this information is not clinically leveraged for CLD diagnosis.
methodsWe developed and evaluated EchoNet-Liver, a deep-learning, computer-vision pipeline that can identify high-quality subcostal images from full echocardiogram studies and detect the presence of cirrhosis and steatotic liver disease (SLD). This retrospective observational study included adult patients from two large urban academic medical centers who received both echocardiography and abdominal imaging - either ultrasound or magnetic resonance imaging (MRI) - within 30 days. The model predictions were compared with diagnoses from clinical evaluations of paired abdominal ultrasound or MRI studies.
resultsA total of 1,596,640 echocardiogram videos from 66,922 studies and 24,276 patients at Cedars-Sinai Medical Center (CSMC) were used to develop EchoNet-Liver. In a held-out CSMC test cohort, EchoNet-Liver detected cirrhosis with an area under the receiver operating characteristic curve (AUROC) of 0.837 (95% confidence interval [CI], 0.828 to 0.848) and SLD with an AUROC of 0.799 (95% CI, 0.788 to 0.811). In a separate test cohort with paired abdominal MRI studies, EchoNet-Liver detected cirrhosis with an AUROC of 0.704 (95% CI, 0.699 to 0.708) and SLD with an AUROC of 0.725 (95% CI, 0.707 to 0.762). In an external test cohort of 106 patients (5280 videos), the model detected cirrhosis with an AUROC of 0.830 (95% CI, 0.799 to 0.859) and SLD with an AUROC of 0.769 (95% CI, 0.733 to 0.813).
conclusionsDeep-learning assessment of clinical echocardiography enables opportunistic screening for SLD and cirrhosis. The application of this algorithm can identify patients who may benefit from further diagnostic testing and treatment for CLD. (Funded by KAKENHI [Japan Society for the Promotion of Science, 24K10526] and others.).
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