NCT07305636已完成不适用
Assessing the Utility of AI Models in MAFLD Diagnosis: Comparison With Traditional Non-Invasive Fibrosis Scores.
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 522
- 试验地点
- 1
- 主要终点
- Measure diagnostic accuracy of AI models in predicting hepatic fibrosis stage (F0-F4)
研究概览
简要总结
This study evaluates the accuracy of artificial intelligence (AI) models using FibroScan and clinical data to predict hepatic fibrosis in Egyptian patients with metabolic-associated fatty liver disease (MAFLD). The performance of the AI models will be compared with conventional noninvasive fibrosis scores (FIB-4, APRI, NAFLD fibrosis score, and FAST). The goal is to improve early, noninvasive diagnosis of fibrosis and reduce reliance on liver biopsy.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Days 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •- Adults ≥18 years.
- •Diagnosed with MAFLD according to international criteria (hepatic steatosis with metabolic dysfunction).
- •Valid FibroScan evaluation with available LSM and CAP values.
排除标准
- •Excessive alcohol intake (>30 g/day for men, >20 g/day for women).
- •Chronic viral hepatitis (HBV or HCV).
- •Autoimmune hepatitis.
- •Known malignancy.
- •Refusal to participate.
结局指标
主要结局
Measure diagnostic accuracy of AI models in predicting hepatic fibrosis stage (F0-F4)
时间窗: At enrollment (single cross-sectional assessment).
次要结局
未报告次要终点
研究者
Nahla Ahmed Khalaf
Lecturer of Tropical Medicine and Infectious diseases
Tanta University
研究点 (1)
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