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

NCT07675525 | Completed

• 1 other identifier: 338/2569(SIRB1)
• Study Type: observational
• Target: 1,351
• Locations: 1 country
• Sites: 1

Brief 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)

Keywords

MASLD; Liver Fibrosis; Deep Learning; Gated Recurrent Unit; Non-invasive Triage; Longitudinal EHR

Outcome Measures

Primary Outcomes (1)

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

  Assessed at end of observation period (December 2022)

Secondary Outcomes (6)

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

  Assessed at end of observation period (December 2022)

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

  Assessed at end of observation period (December 2022)

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

  Assessed at end of observation period (December 2022)

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

  Assessed at end of observation period (December 2022)

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

  Assessed at end of observation period (December 2022)

Study Arms (2)

Primary longitudinal cohort (≥2 visits)

Diagnostic Test: Longitudinal electronic health record analysis

Singleton sensitivity analysis cohort (1 visit)

Diagnostic Test: Longitudinal electronic health record analysis

Interventions

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 longitudinal cohort (≥2 visits); Singleton sensitivity analysis cohort (1 visit)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adults with confirmed metabolic dysfunction-associated steatotic liver disease (MASLD) receiving outpatient hepatology care at Siriraj Hospital, a 2,500-bed tertiary academic medical centre in Bangkok, Thailand. The population reflects a high metabolic comorbidity burden typical of urban Thai patients, with elevated rates of type 2 diabetes, obesity, and cardiometabolic multimorbidity.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Siriraj Hospital: lead

Study Sites (1)

Faculty of Medicine Siriraj Hospital

Bangkok Noi, Bangkok, 10700, Thailand

Location

MeSH Terms

Conditions

Liver Cirrhosis

Condition Hierarchy (Ancestors)

Liver Diseases; Digestive System Diseases; Fibrosis; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: June 18, 2026
• First Posted: June 30, 2026
• Study Start: January 1, 2018
• Primary Completion: December 31, 2022
• Study Completion: June 16, 2024
• Last Updated: June 30, 2026

Record last verified: 2026-06

Locations

TH (1)