Assessing the Utility of AI Models in MAFLD Diagnosis: Comparison With Traditional Non-Invasive Fibrosis Scores.
Assessing the Utility of AI Models in MAFLD Diagnosis: Comparison With Traditional Non-Invasive Fibrosis Scores.
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.
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.