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.
調査の概要
研究の種類
入学 (実際)
連絡先と場所
研究場所
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Bangkok
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Bangkok Noi、Bangkok、タイ、10700
- Faculty of Medicine Siriraj Hospital
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参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
サンプリング方法
調査対象母集団
説明
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
研究計画
研究はどのように設計されていますか？
デザインの詳細
コホートと介入
グループ/コホート |
介入・治療 |
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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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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
時間枠 |
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Area under the receiver operating characteristic curve (AUROC) for significant fibrosis (F≥2) identification
時間枠：Assessed at end of observation period (December 2022)
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Assessed at end of observation period (December 2022)
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二次結果の測定
結果測定 |
時間枠 |
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Sensitivity-constrained positive predictive value (PPV) for significant fibrosis (F≥2) at optimised classification threshold
時間枠：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
時間枠：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)
時間枠：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
時間枠：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
時間枠：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
時間枠：Assessed at end of observation period (December 2022)
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Assessed at end of observation period (December 2022)
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協力者と研究者
スポンサー
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (実際)
研究の完了 (実際)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
その他の研究ID番号
- 338/2569(SIRB1)
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
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