CompletedNot Applicable

AI Models vs Non-Invasive Fibrosis Scores in MAFLD Diagnosis

Egypt522 participantsStarted 2025-05-13

Plain-language summary

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.

Who can participate

Age range18 Days

SexALL

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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: * Excessive alcohol intake (\>30 g/day for men, \>20 g/day for women). Chronic viral hepatitis (HBV or HCV). Autoimmune hepatitis. Known malignancy. Pregnancy. Refusal to participate.

What they're measuring

Measure diagnostic accuracy of AI models in predicting hepatic fibrosis stage (F0-F4)

Timeframe: At enrollment (single cross-sectional assessment).

Trial details

NCT IDNCT07305636

SponsorTanta University

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2025-08-30

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