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AI Models vs Non-Invasive Fibrosis Scores in MAFLD Diagnosis

NCT07305636 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 522

Last updated 2025-12-26

No results posted yet for this study

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.

Conditions

  • MAFLD
  • AI (Artificial Intelligence)

Sponsors & Collaborators

  • Tanta University

    lead OTHER

Eligibility

Min Age
18 Days
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-05-13
Primary Completion
2025-08-30
Completion
2025-11-30

Countries

  • Egypt

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07305636 on ClinicalTrials.gov