MASLD (Metabolic Dysfunction-Associated Steatotic Liver Disease)NIMIT-AINCT07675525
Deep Learning for Liver Fibrosis Triage in MASLD Using Longitudinal Electronic Health Records
An observational study of MASLD (Metabolic Dysfunction-Associated Steatotic Liver Disease), run by Siriraj Hospital at 1 site in Thailand.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- MASLD (Metabolic Dysfunction-Associated Steatotic Liver Disease)
- What is being tested?
- Longitudinal electronic health record analysis
- How many people
- 1,351 participants
- Where
- 1 siteThailand
- Study dates
- 1 Jan 2018to 16 Jun 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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 locations and contacts
1 site in 1 country.
Thailand1 site
Faculty of Medicine Siriraj Hospital
Bangkok Noi, Bangkok, 10700
About this study
Official title: 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.
Read more
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.
- Being tested
- Longitudinal electronic health record analysis
- Keywords
- MASLD, Liver Fibrosis, Deep Learning, Gated Recurrent Unit, Non-invasive Triage, Longitudinal EHR
- Medical subject headings
- Liver Cirrhosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Longitudinal electronic health record analysisDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| Primary longitudinal cohort (≥2 visits) | Not described Receives: Diagnostic Test: Longitudinal electronic health record analysis | |
| Singleton sensitivity analysis cohort (1 visit) | Not described Receives: Diagnostic Test: Longitudinal electronic health record analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the receiver operating characteristic curve (AUROC) for significant fibrosis (F≥2) identification
Measured: 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
Measured: 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
Measured: Assessed at end of observation period (December 2022)
Net reclassification improvement (NRI) of NIMIT-AI versus FIB-4 at guideline-recommended threshold (1.30)
Measured: Assessed at end of observation period (December 2022)
Integrated discrimination improvement (IDI) of NIMIT-AI versus FIB-4
Measured: Assessed at end of observation period (December 2022)
Attention weight distribution across visit positions for temporal interpretability of NIMIT-AI predictions
Measured: Assessed at end of observation period (December 2022)
SHAP (SHapley Additive exPlanations) feature importance values for global model interpretability across fibrosis classes
Measured: Assessed at end of observation period (December 2022)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jun 2026
- Study start1 Jan 2018
- Primary completion31 Dec 2022
- Study completion16 Jun 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for MASLD (Metabolic Dysfunction-Associated Steatotic Liver Disease)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,351 participants (actual)
Sponsor and investigators
- Lead sponsor
- Siriraj Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Tawesak Tanwandee, Siriraj Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07675525
- Sponsor's protocol ID
- 338/2569(SIRB1)
- Acronym
- NIMIT-AI
- Status last verified
- Jun 2026
- First submitted
- 18 Jun 2026
- Last updated
- 30 Jun 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 30 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- NIMIT-AI: Neural Inference for Metabolic-liver Integrated Trajectories: Leveraging Deep Learning to Enhance Reliability in MASLD Triage
- ClinicalTrials.gov ID
- NCT07675525
- Acronym / sponsor ID
- NIMIT-AI / 338/2569(SIRB1)
- Keywords
- MASLD, Liver Fibrosis, Deep Learning, Gated Recurrent Unit, Non-invasive Triage, Longitudinal EHR
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,351 participants (actual)
- Lead sponsor
- Siriraj HospitalUniversity, hospital or other
- Study start
- 1 Jan 2018 (actual)
- Primary completion
- 31 Dec 2022 (actual)
- Study completion
- 16 Jun 2024 (actual)
- First posted
- 30 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Longitudinal electronic health record analysisNIMIT-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. | Diagnostic test | Primary longitudinal cohort (≥2 visits), Singleton sensitivity analysis cohort (1 visit) |
Primary outcome
Area under the receiver operating characteristic curve (AUROC) for significant fibrosis (F≥2) identification
Time frame: Assessed at end of observation period (December 2022)
6 secondary outcomes
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)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Jun 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of MASLD (Metabolic Dysfunction-Associated Steatotic Liver Disease), run by Siriraj Hospital at 1 site in Thailand.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 30 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Thailand. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the receiver operating characteristic curve (AUROC) for significant fibrosis (F≥2) identification (time frame: Assessed at end of observation period (December 2022)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Jun 2026.