Deep Learning for Liver Fibrosis Triage in MASLD Using Longitudinal Electronic Health Records (NIMIT-AI)
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.
Přehled studie
Postavení
Intervence / Léčba
Typ studie
Zápis (Aktuální)
Kontakty a umístění
Studijní místa
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Bangkok
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Bangkok Noi, Bangkok, Thajsko, 10700
- Faculty of Medicine Siriraj Hospital
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Kritéria účasti
Kritéria způsobilosti
Věk způsobilý ke studiu
- Dospělý
- Starší dospělý
Přijímá zdravé dobrovolníky
Metoda odběru vzorků
Studijní populace
Popis
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
Studijní plán
Jak je studie koncipována?
Detaily designu
Kohorty a intervence
Skupina / kohorta |
Intervence / Léčba |
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Primary longitudinal cohort (≥2 visits)
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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.
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Singleton sensitivity analysis cohort (1 visit)
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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.
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Co je měření studie?
Primární výstupní opatření
Měření výsledku |
Časové okno |
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Area under the receiver operating characteristic curve (AUROC) for significant fibrosis (F≥2) identification
Časové okno: Assessed at end of observation period (December 2022)
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Assessed at end of observation period (December 2022)
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Sekundární výstupní opatření
Měření výsledku |
Časové okno |
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Sensitivity-constrained positive predictive value (PPV) for significant fibrosis (F≥2) at optimised classification threshold
Časové okno: Assessed at end of observation period (December 2022)
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Assessed at end of observation period (December 2022)
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Diagnostic performance for compensated advanced chronic liver disease (F3-F4 cACLD) reported as one-vs-rest AUROC
Časové okno: Assessed at end of observation period (December 2022)
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Assessed at end of observation period (December 2022)
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Net reclassification improvement (NRI) of NIMIT-AI versus FIB-4 at guideline-recommended threshold (1.30)
Časové okno: Assessed at end of observation period (December 2022)
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Assessed at end of observation period (December 2022)
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Integrated discrimination improvement (IDI) of NIMIT-AI versus FIB-4
Časové okno: Assessed at end of observation period (December 2022)
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Assessed at end of observation period (December 2022)
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Attention weight distribution across visit positions for temporal interpretability of NIMIT-AI predictions
Časové okno: Assessed at end of observation period (December 2022)
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Assessed at end of observation period (December 2022)
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SHAP (SHapley Additive exPlanations) feature importance values for global model interpretability across fibrosis classes
Časové okno: Assessed at end of observation period (December 2022)
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Assessed at end of observation period (December 2022)
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Spolupracovníci a vyšetřovatelé
Sponzor
Termíny studijních záznamů
Hlavní termíny studia
Začátek studia (Aktuální)
Primární dokončení (Aktuální)
Dokončení studie (Aktuální)
Termíny zápisu do studia
První předloženo
První předloženo, které splnilo kritéria kontroly kvality
První zveřejněno (Aktuální)
Aktualizace studijních záznamů
Poslední zveřejněná aktualizace (Aktuální)
Odeslaná poslední aktualizace, která splnila kritéria kontroly kvality
Naposledy ověřeno
Více informací
Termíny související s touto studií
Klíčová slova
Další relevantní podmínky MeSH
Další identifikační čísla studie
- 338/2569(SIRB1)
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