NCT06061640

The Potential Value and Impact of Diagnostic Biomarkers for MAFLD Using Machine Learning Methods

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 500 (estimated)
• Sponsor: The First Affiliated Hospital of Zhejiang Chinese Medical University · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: Accepted

Summary

This is a case-control study that aims to build a predictive model for MAFLD based on machine learning.

Detailed description

Metabolic dysfunction-associated fatty liver disease (MAFLD) also known as non-alcoholic fatty liver disease (NAFLD), is one of the most prevalent liver diseases worldwide with high prevalence and economic burden, which affects 25% of global adult population. Despite extensive research on understanding the inner pathophysiology of MAFLD, it still keep growing with no approval therapy. Therefore, preventive measures are particularly important in diagnosing MAFLD. So far the liver biopsy is still the gold standard for diagnosis of MAFLD, however considering the invasive process and potential risks, it still has low acceptance for asymptomatic patients, thus non-invasive methods are necessary for this reason. The purpose of this study is to establish a prediction model to identify MAFLD patients, which can accurately predict whether the participants have MAFLD according to the relevant metabolic indicators of the participants, without the need for invasive examinations such as tissue biopsy.

Conditions

• Metabolic Dysfunction-associated Fatty Liver Disease

Timeline

• Start date: 2023-06-01
• Primary completion: 2024-06-30
• Completion: 2024-12-31
• First posted: 2023-09-29
• Last updated: 2023-09-29

Locations

1 site across 1 country: China