Automatic Diagnosis of Early Esophageal Squamous Neoplasia Using Probe-based Confocal Laser Endomicroscopy With Artificial Intelligence
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 57
- 试验地点
- 1
- 主要终点
- The diagnosis efficiency of Artificial Intelligence
研究概览
简要总结
Detection and differentiation of esophageal squamous neoplasia (ESN) are of value in improving patient outcomes. Probe-based confocal laser endomicroscopy (pCLE) can diagnose ESN accurately.However this requires much experience, which limits the application of pCLE. The investigators designed a computer-aided diagnosis program using deep neural network to make diagnosis automatically in pCLE examination and contrast its performance with endoscopists.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •aged between 18 and 80;
- •agree to give written informed consent；
排除标准
- •advanced esophageal squamous cell carcinoma or esophageal stenosis;
- •having no suspicious lesion of ESN found by WLE and IEE
- •known allergy to fluorescein sodium;
- •having coagulopathy or impaired renal function;
- •being pregnant or breastfeeding.
结局指标
主要结局
The diagnosis efficiency of Artificial Intelligence
时间窗: 3 years
The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing esophageal mucosal disease on real-time pCLE examination.
次要结局
- Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists(1 month)
研究者
Yanqing Li
Vice president of QiLu Hospital
Shandong University