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临床试验/NCT04384575
NCT04384575已完成不适用

Study on the Effectiveness of Gastroscope Operation Quality Control Based on Artificial Intelligence Technology

Peking University1 个研究点 分布在 1 个国家目标入组 1,570 人开始时间: 2020年2月22日最近更新:
适应症

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

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

发起方
Peking University
申办方类型
Other
责任方
Principal Investigator
主要研究者

Peng Yuan

MD,PHD

Peking University

研究点 (1)

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