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.
연구 개요
연구 유형
등록 (실제)
연락처 및 위치
연구 장소
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Bangkok
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Bangkok Noi, Bangkok, 태국, 10700
- Faculty of Medicine Siriraj Hospital
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참여기준
자격 기준
공부할 수 있는 나이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
샘플링 방법
연구 인구
설명
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
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
코호트 및 개입
그룹/코호트 |
개입 / 치료 |
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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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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
기간 |
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Area under the receiver operating characteristic curve (AUROC) for significant fibrosis (F≥2) identification
기간: Assessed at end of observation period (December 2022)
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Assessed at end of observation period (December 2022)
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2차 결과 측정
결과 측정 |
기간 |
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Sensitivity-constrained positive predictive value (PPV) for significant fibrosis (F≥2) at optimised classification threshold
기간: 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
기간: 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)
기간: 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
기간: 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
기간: 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
기간: Assessed at end of observation period (December 2022)
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Assessed at end of observation period (December 2022)
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공동 작업자 및 조사자
스폰서
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (실제)
연구 완료 (실제)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
기타 연구 ID 번호
- 338/2569(SIRB1)
약물 및 장치 정보, 연구 문서
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미국 FDA 규제 기기 제품 연구
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