Deep Learning for Liver Fibrosis Triage in MASLD Using Longitudinal Electronic Health Records (NIMIT-AI)

Siriraj Hospital

Status: Completed

Conditions: MASLD (Metabolic Dysfunction-Associated Steatotic Liver Disease)

Treatments: Diagnostic Test: Longitudinal electronic health record analysis

Study type: Observational

Funder types: Other

Identifiers

NCT07675525

338/2569(SIRB1)

Details and patient eligibility

About

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.

Enrollment: 1,351 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

1,351 participants in 2 patient groups

Primary longitudinal cohort (≥2 visits)

Treatment:

Diagnostic Test: Longitudinal electronic health record analysis

Singleton sensitivity analysis cohort (1 visit)

Treatment:

Diagnostic Test: Longitudinal electronic health record analysis