NIMIT-AI: Neural Inference for Metabolic-liver Integrated Trajectories: Leveraging Deep Learning to Enhance Reliability in MASLD Triage
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 1,351
- 试验地点
- 1
- 主要终点
- Area under the receiver operating characteristic curve (AUROC) for significant fibrosis (F≥2) identification
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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
结局指标
主要结局
Area under the receiver operating characteristic curve (AUROC) for significant fibrosis (F≥2) identification
时间窗: Assessed at end of observation period (December 2022)
次要结局
- Sensitivity-constrained positive predictive value (PPV) for significant fibrosis (F≥2) at optimised classification threshold(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(Assessed at end of observation period (December 2022))
- Net reclassification improvement (NRI) of NIMIT-AI versus FIB-4 at guideline-recommended threshold (1.30)(Assessed at end of observation period (December 2022))
- Integrated discrimination improvement (IDI) of NIMIT-AI versus FIB-4(Assessed at end of observation period (December 2022))
- Attention weight distribution across visit positions for temporal interpretability of NIMIT-AI predictions(Assessed at end of observation period (December 2022))
- SHAP (SHapley Additive exPlanations) feature importance values for global model interpretability across fibrosis classes(Assessed at end of observation period (December 2022))
研究者
Tawesak Tanwandee
Professor
Siriraj Hospital