An observational study in MASLD (Metabolic Dysfunction-Associated Steatotic Liver Disease), sponsored by Siriraj Hospital. Completed at 1 site in Thailand. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-06-30.
Sponsored by Siriraj Hospital · Observational
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.
1,642 studies on the registry are indexed under Liver Cirrhosis; 358 are open to participants now.
This study's enrollment of 1,351 is above the median of 151 across 587 observational studies indexed under Liver Cirrhosis.
Browse Liver Cirrhosis studies →Siriraj Hospital is the lead sponsor of 95 studies on the registry; 32 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
Exclusion Criteria:
Diagnostic Test: Longitudinal electronic health record analysis
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.
Area under the receiver operating characteristic curve (AUROC) for significant fibrosis (F≥2) identification
Time frame: Assessed at end of observation period (December 2022)
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)
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)
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)
Integrated discrimination improvement (IDI) of NIMIT-AI versus FIB-4
Time frame: Assessed at end of observation period (December 2022)
Attention weight distribution across visit positions for temporal interpretability of NIMIT-AI predictions
Time frame: Assessed at end of observation period (December 2022)
SHAP (SHapley Additive exPlanations) feature importance values for global model interpretability across fibrosis classes
Time frame: Assessed at end of observation period (December 2022)
This study is completed, as verified in Jun 2026. You cannot join it, but the record below documents what was studied.
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