AI Models vs Non-Invasive Fibrosis Scores in MAFLD Diagnosis

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.

NCT Number	NCT07305636
Lead Sponsor	Tanta University
Conditions	MAFLD, AI (Artificial Intelligence)
Enrollment	522 participants
Start Date	2025-05-13
Primary Completion	2025-08-30 (estimated)
Study Completion	2025-11-30 (estimated)
Updated on ClinicalTrials.gov	2025-12-26