AI Models vs Non-Invasive Fibrosis Scores in MAFLD Diagnosis (MAFLD-AI)

Tanta University

Status: Completed

Conditions: MAFLD; AI (Artificial Intelligence)

Study type: Observational

Funder types: Other

Identifiers

NCT07305636

TANTAU-MAFLD-AI-2025-01

Details and patient eligibility

About

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.

Enrollment: 522 patients

Sex: All

Ages: 18+ days old

Volunteers: No Healthy Volunteers

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.