AI-Driven Multimodal Imaging Integration for Diagnosis and Prognostication of Digestive System Diseases
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 5,000
- 试验地点
- 1
- 主要终点
- The area under the ROC curve (AUC) to assess the performance of diagnostic model.
研究概览
简要总结
The goal of this observational, retrospective and prospective study is to develop a noninvasive disease assessment system by leveraging artificial intelligence (AI) to comprehensively analyze multi-modal imaging features, including magnetic resonance enterography (MRE) and computed tomography enterography (CTE), for the diagnosis and prognostication of digestive diseases. To this end, the investigators retrospectively enrolled imaging, endoscopic, and clinical data from 21 centers across China to construct and iteratively optimize the AI model. The model's performance will be prospectively validated in two centers, and its accuracy in lesion localization will be verified through real-world deployment in endoscopy suites.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Other
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Patients with multimodal-confirmed diagnoses (clinical, imaging, endoscopic, and pathological) of:
- •Inflammatory bowel disease (IBD; Crohn's disease or ulcerative colitis)
- •Intestinal tuberculosis
- •Behçet's disease
- •Availability of ≥1 technically adequate CT or MR scan with high-quality colonoscopy performed within ±1 month of imaging.
排除标准
- •・ Suboptimal imaging quality (e.g., low-dose artifacts, metal artifacts)
- •Inadequate bowel preparation for endoscopy
- •Incomplete examinations due to poor tolerance
结局指标
主要结局
The area under the ROC curve (AUC) to assess the performance of diagnostic model.
时间窗: 6 months
After baseline MR or CT scanning, patients were followed up.
次要结局
未报告次要终点
研究者
Xuehua Li
associate chief physician
First Affiliated Hospital, Sun Yat-Sen University