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This study evaluates the accuracy of artificial intelligence (AI) models using FibroScan and clinical data to predict hepatic fibrosis in Egyptian patients with metabolic-associated fatty liver disease (MAFLD). The performance of the AI models will be compared with conventional noninvasive fibrosis scores (FIB-4, APRI, NAFLD fibrosis score, and FAST). The goal is to improve early, noninvasive diagnosis of fibrosis and reduce reliance on liver biopsy.
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| Measure | Description | Time Frame |
|---|---|---|
| Measure diagnostic accuracy of AI models in predicting hepatic fibrosis stage (F0-F4) | At enrollment (single cross-sectional assessment). |
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Inclusion Criteria:
- Adults ≥18 years.
Diagnosed with MAFLD according to international criteria (hepatic steatosis with metabolic dysfunction).
Valid FibroScan evaluation with available LSM and CAP values.
Exclusion Criteria:
Chronic viral hepatitis (HBV or HCV).
Autoimmune hepatitis.
Known malignancy.
Pregnancy.
Refusal to participate.
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Adult Egyptian patients (≥18 years) diagnosed with metabolic associated fatty liver disease (MAFLD) according to international criteria, recruited from the outpatient clinic and FibroScan unit of the Tropical Medicine Department, Faculty of Medicine, Tanta University.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Faculty of Medicine | Tanta | Egypt |
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