AI Models vs Non-Invasive Fibrosis Scores in MAFLD Diagnosis

Tanta University522 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

522

Conditions

MAFLD AI (Artificial Intelligence)

Eligibility

Sex: ALLMin age: 18 Days

Medical Language ↔ Plain English

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.

Locations (1)

Faculty of Medicine

Tanta, Egypt

Outcomes

Primary Outcomes

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

Time frame: At enrollment (single cross-sectional assessment).

Data from ClinicalTrials.gov

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