Opportunistic Screening of Chronic Liver Disease with Deep-Learning–Enhanced Echocardiography
Bibliographic record
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".