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

December 12, 2025 updated by: Nahla Ahmed Khalaf, Tanta University

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.

Study Overview

Status

Completed

Conditions

Study Type

Observational

Enrollment (Actual)

522

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Egypt
      • Tanta, Egypt
        • Faculty of medicine

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

Study Population

Adult Egyptian patients (≥18 years) diagnosed with metabolic associated fatty liver disease (MAFLD) according to international criteria, recruited from the outpatient clinic and FibroScan unit of the Tropical Medicine Department, Faculty of Medicine, Tanta University.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
Measure diagnostic accuracy of AI models in predicting hepatic fibrosis stage (F0-F4)
Time Frame: At enrollment (single cross-sectional assessment).
At enrollment (single cross-sectional assessment).

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Tanta University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 13, 2025

Primary Completion (Actual)

August 30, 2025

Study Completion (Actual)

November 30, 2025

Study Registration Dates

First Submitted

December 12, 2025

First Submitted That Met QC Criteria

December 12, 2025

First Posted (Actual)

December 26, 2025

Study Record Updates

Last Update Posted (Actual)

December 26, 2025

Last Update Submitted That Met QC Criteria

December 12, 2025

Last Verified

December 1, 2025

More Information

Terms related to this study

Other Study ID Numbers

  • TANTAU-MAFLD-AI-2025-01

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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