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Deep Learning for Liver Fibrosis Triage in MASLD Using Longitudinal Electronic Health Records

NCT07675525 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1351

Last updated 2026-06-30

No results posted yet for this study

Summary

This study looks at a new computer program called NIMIT-AI (Neural Inference for Metabolic-liver Integrated Trajectories, Artificial Intelligence) that helps doctors find liver scarring early in patients with fatty liver disease.

Fatty liver disease, also called metabolic dysfunction-associated steatotic liver disease (MASLD), is a common condition where fat builds up in the liver. Over time, this can cause scarring (fibrosis). Finding scarring early helps doctors treat it before it gets worse.

Right now, doctors use a blood test score called FIB-4 to check for scarring. But this score misses many patients and cannot be calculated when blood test results are incomplete.

NIMIT-AI works differently. It reads a patient's blood test results over multiple visits, not just one visit, to spot patterns that suggest liver scarring. It was tested on 969 patients seen at Siriraj Hospital in Bangkok, Thailand between 2018 and 2022.

In testing, NIMIT-AI found liver scarring more accurately than FIB-4. It also worked even when some blood test results were missing, which happens often in real clinics.

This study did not ask patients to do anything extra. It used health records that were already collected as part of regular care.

Conditions

  • MASLD (Metabolic Dysfunction-Associated Steatotic Liver Disease)

Interventions

DIAGNOSTIC_TEST

Longitudinal electronic health record analysis

NIMIT-AI, a gated recurrent unit deep learning model, analyzed serial outpatient laboratory results from electronic health records collected over a 5-year observation window (2018-2022) at Siriraj Hospital. The model processed up to 10 sequential visits per patient using 18 clinical features including liver enzymes, metabolic markers, comorbidity flags, and medication exposures to predict liver fibrosis stage without requiring elastography.

Sponsors & Collaborators

  • Siriraj Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-01-01
Primary Completion
2022-12-31
Completion
2024-06-16

Countries

  • Thailand

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 NCT07675525 on ClinicalTrials.gov