Ultrasound Elastography Used for Preventive Non-Invasive Screening in Early Detection of Liver Fibrosis
Bibliographic record
Abstract
BACKGROUND: Early discovery of liver fibrosis is becoming more popular because of enhanced incidence of hepatocellular carcinoma. Ultrasound-based liver elastography is a method used to approve suspected liver fibrosis or cirrhosis. We assessed the clinical usefulness of acoustic radiation force impulse shear wave elasticity imaging (ARFI-SWEI) as a preventive screening method to uncover fibrosis. METHODS: We screened 382 patients by native routine sonography for abnormal liver results and divided them into six groups: group 1: normal liver, groups 2-4: fatty liver grade I-III, group 5: liver cirrhosis, and group 6: inhomogenic liver tissue. Then ARFI-SWEI was performed and the results were compared with published shear wave velocity cut-off values that were predictive of each fibrosis stage (F0-4). A control group consisted of 20 healthy volunteers. RESULTS: The part of liver fibrosis ≥ F2 was in groups 1-4: 20-32%, group 5: 100%, and group 6: 91%. Main causes for fibrosis stage ≥ F2 were (non)-alcoholic steatohepatitis, chronic viral or autoimmune hepatitis and chronic heart failure. CONCLUSIONS: Screening of the liver tissue in b-mode ultrasound can underestimate possible liver fibrosis; by using ARFI-SWEI, liver fibrosis can be uncovered early. It is a suitable preventive method comparable to colonoscopy for colon cancer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".