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Completed

NCT Number: NCT07675525

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

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.

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Key information

Conditions

MASLD (Metabolic Dysfunction-Associated Steatotic Liver Disease) Digestive System Diseases Fibrosis Liver Cirrhosis Liver Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Faculty of Medicine Siriraj Hospital

Bangkok Noi, Bangkok, 10700, Thailand

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Age ≥18 years at index visit
  • Confirmed MASLD diagnosis per Delphi consensus criteria
  • At least one outpatient visit with concurrent laboratory data and FibroScan liver stiffness measurement within observation window (2018-2022)
  • Receiving care at Division of Gastroenterology, Faculty of Medicine Siriraj Hospital, Mahidol University

Exclusion criteria

  • Alternative chronic liver disease aetiology (autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, Wilson's disease, haemochromatosis)
  • Chronic viral hepatitis (hepatitis B or C surface antigen positivity)
  • Prior liver transplantation
  • Active extrahepatic malignancy at baseline
  • Insufficient longitudinal data for outcome ascertainment

Treatment and study plan

Longitudinal electronic health record analysis

Diagnostic Test

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.

Primary outcomes

  1. Area under the receiver operating characteristic curve (AUROC) for significant fibrosis (F≥2) identification

    Time frame: Assessed at end of observation period (December 2022)

Secondary outcomes

  1. Sensitivity-constrained positive predictive value (PPV) for significant fibrosis (F≥2) at optimised classification threshold

    Time frame: Assessed at end of observation period (December 2022)

  2. Diagnostic performance for compensated advanced chronic liver disease (F3-F4 cACLD) reported as one-vs-rest AUROC

    Time frame: Assessed at end of observation period (December 2022)

  3. Net reclassification improvement (NRI) of NIMIT-AI versus FIB-4 at guideline-recommended threshold (1.30)

    Time frame: Assessed at end of observation period (December 2022)

  4. Integrated discrimination improvement (IDI) of NIMIT-AI versus FIB-4

    Time frame: Assessed at end of observation period (December 2022)

  5. Attention weight distribution across visit positions for temporal interpretability of NIMIT-AI predictions

    Time frame: Assessed at end of observation period (December 2022)

  6. SHAP (SHapley Additive exPlanations) feature importance values for global model interpretability across fibrosis classes

    Time frame: Assessed at end of observation period (December 2022)

Sponsors and collaborators

Lead sponsor

Siriraj Hospital

Other

Registry information

Official study title

NIMIT-AI: Neural Inference for Metabolic-liver Integrated Trajectories: Leveraging Deep Learning to Enhance Reliability in MASLD Triage

Acronym: NIMIT-AI

Important dates

Study start
2018
Primary completion
2022
Study completion
2024
First posted
Jun 30, 2026
Registry last updated
Jun 30, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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