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

26. juni 2026 opdateret af: Tawesak Tanwandee, Siriraj Hospital

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

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.

Studieoversigt

Status

Afsluttet

Betingelser

Intervention / Behandling

Undersøgelsestype

Observationel

Tilmelding (Faktiske)

1351

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiesteder

  • Thailand
    • Bangkok
      • Bangkok Noi, Bangkok, Thailand, 10700
        • Faculty of Medicine Siriraj Hospital

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

N/A

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

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.

Beskrivelse

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

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Kohorter og interventioner

Gruppe / kohorte
Intervention / Behandling
Primary longitudinal cohort (≥2 visits)
Diagnostisk 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.
Singleton sensitivity analysis cohort (1 visit)
Diagnostisk 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.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Tidsramme
Area under the receiver operating characteristic curve (AUROC) for significant fibrosis (F≥2) identification
Tidsramme: Assessed at end of observation period (December 2022)
Assessed at end of observation period (December 2022)

Sekundære resultatmål

Resultatmål
Tidsramme
Sensitivity-constrained positive predictive value (PPV) for significant fibrosis (F≥2) at optimised classification threshold
Tidsramme: Assessed at end of observation period (December 2022)
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
Tidsramme: Assessed at end of observation period (December 2022)
Assessed at end of observation period (December 2022)
Net reclassification improvement (NRI) of NIMIT-AI versus FIB-4 at guideline-recommended threshold (1.30)
Tidsramme: Assessed at end of observation period (December 2022)
Assessed at end of observation period (December 2022)
Integrated discrimination improvement (IDI) of NIMIT-AI versus FIB-4
Tidsramme: Assessed at end of observation period (December 2022)
Assessed at end of observation period (December 2022)
Attention weight distribution across visit positions for temporal interpretability of NIMIT-AI predictions
Tidsramme: Assessed at end of observation period (December 2022)
Assessed at end of observation period (December 2022)
SHAP (SHapley Additive exPlanations) feature importance values for global model interpretability across fibrosis classes
Tidsramme: Assessed at end of observation period (December 2022)
Assessed at end of observation period (December 2022)

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Siriraj Hospital

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

1. januar 2018

Primær færdiggørelse (Faktiske)

31. december 2022

Studieafslutning (Faktiske)

16. juni 2024

Datoer for studieregistrering

Først indsendt

18. juni 2026

Først indsendt, der opfyldte QC-kriterier

26. juni 2026

Først opslået (Faktiske)

30. juni 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

30. juni 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

26. juni 2026

Sidst verificeret

1. juni 2026

Mere information

Begreber relateret til denne undersøgelse

Nøgleord

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • 338/2569(SIRB1)

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