Study on the Effectiveness of Gastroscope Operation Quality Control Based on Artificial Intelligence Technology
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 1,570
- 试验地点
- 1
- 主要终点
- Accuracy
研究概览
简要总结
This study aims to construct a real-time quality monitoring system based on artificial intelligence technology.
详细描述
Gastroscopy plays an important role in the detection and diagnosis of upper gastrointestinal diseases. It is necessary for endoscopists to operate gastroscope according to the standardized process, in order to avoid missing early lesions. However, with the rapid increase in the number of endoscopies, the workload of endoscopists increases further. High workload reduces the quality of endoscopy, resulting in incomplete observation of anatomical parts that are easy to be missed in the process of gastroscopy. There are significant differences in the operation level of different endoscopists. Therefore, carrying out artificial intelligence methods has good academic research and practical value for improving the quality of endoscopic diagnosis and treatment.
Artificial intelligence devices need to use a large number of endoscopic images, based on this, we intends to collect endoscopic image data from our hospitals for training and validation of the model.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 75 Years(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Patiens aged 18 years or above undergoing gastroscopy;
- •Be able to read, understand and sign informed consent;
排除标准
- •Patients with absolute contraindications to endoscopy examination;
- •pregnant women;
- •previous history of gastric surgery;
- •the researcher considers that the subject is not suitable for clinical trial.
结局指标
主要结局
Accuracy
时间窗: 2020.2.22-2020.7.1
Calculate the accuracy of AI's judgment on images
Sensitivity
时间窗: 2020.2.22-2020.7.1
number of images in which AI correctly diagnosed positive/all images with positive
Specificity
时间窗: 2020.2.22-2020.7.1
number of images in which AI correctly diagnosed negative/all images negative
次要结局
未报告次要终点
研究者
Peng Yuan
MD,PHD
Peking University
