Opportunistic Screening of Chronic Liver Disease with Deep-Learning-Enhanced Echocardiography

Chronic 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.

We 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.

A 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).

Deep-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.).