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临床试验/NCT04071678
NCT04071678Unknown不适用

A Randomized Controlled Multicenter Study of Artificial Intelligence Assisted Digestive Endoscopy

Second Affiliated Hospital, School of Medicine, Zhejiang University1 个研究点 分布在 1 个国家目标入组 3,600 人开始时间: 2019年8月1日最近更新:
适应症

试验速览

阶段
不适用
发起方
入组人数
3,600
试验地点
1
主要终点
Changes of detection rate of digestive tract lesions assisted by artificial intelligence gastroenteroscopy

研究概览

简要总结

Digestive endoscopy center of the second affiliated hospital of medical college of zhejiang university and engineers of naki medical co., ltd. in Hong Kong independently developed an ai-assisted diagnostic model of digestive endoscopy in the early stage, namely the deep learning model.The deep learning model through the early stage of the study, is able to identify lesions of digest tract.The sensitivity for the diagnosis of some diseases, such as colon polyps, is 99%. On the one hand, this auxiliary diagnostic model can guide endoscopic examination for beginners; on the other hand, it can improve the detection rate of lesions and reduce the rate of missed diagnosis; on the other hand, the overall operating efficiency of the endoscopic center is improved, which is conducive to the quality control of endoscopic examination. Now the AI-assisted diagnostic model has been further improved, and it is planned to carry out further clinical verification in the digestive endoscopy center of our hospital. It is connected to the endoscopic system of our hospital and used simultaneously with the existing image-text system of endoscopy to compare the practicability, sensitivity and specificity of AI-assisted diagnosis model in the diagnosis of digestive tract diseases, and focus on the quality control of endoscopic examination.

详细描述

Digestive endoscopy center of the second affiliated hospital of medical college of zhejiang university and engineers of naki medical co., ltd. in Hong Kong independently developed an ai-assisted diagnostic model of digestive endoscopy in the early stage, namely the deep learning model。The deep learning model through the early stage of the study, is able to identify lesions of colon polyps, colorectal cancer, colorectal apophysis lesions, colonic diverticulum, ulcerative colitis, gastric ulcer, gastric polyps, submucosal uplift, reflux esophagitis, esophageal ulcer, esophageal polyp, esophageal erosion, esophageal ectopic gastric mucosa and esophagus varicosity, esophageal cancer, esophageal papilloma, etc.The sensitivity for the diagnosis of some diseases, such as colon polyps, is 99%. On the one hand, this auxiliary diagnostic model can guide endoscopic examination for beginners; on the other hand, it can improve the detection rate of lesions and reduce the rate of missed diagnosis; on the other hand, the overall operating efficiency of the endoscopic center is improved, which is conducive to the quality control of endoscopic examination. Now the AI-assisted diagnostic model has been further improved, and it is planned to carry out further clinical verification in the digestive endoscopy center of our hospital. It is connected to the endoscopic system of our hospital and used simultaneously with the existing image-text system of endoscopy to compare the practicability, sensitivity and specificity of AI-assisted diagnosis model in the diagnosis of digestive tract diseases, and focus on the quality control of endoscopic examination.

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 —(Adult, Older Adult)
性别
All
接受健康志愿者
否

入选标准

  • •Voluntarily sign the informed consent for this study
  • •Stable vital signs
  • •Over 18 years old
  • •Patients requiring painless gastroenteroscopy for various reasons

排除标准

  • •Unable or unwilling to sign a consent form, or unable to follow research procedures
  • •have contraindications to painless gastroenteroscopy
  • •Vital signs are unstable
  • •The lesions have been identified by gastroenteroscopy in other hospitals, which is to further confirm the patients who come to our hospital for endoscopic examination
  • •Endoscopic treatment, such as polypectomy, pylorus narrow dilatation and so on

结局指标

主要结局

Changes of detection rate of digestive tract lesions assisted by artificial intelligence gastroenteroscopy

时间窗: 2 years

Endoscopic examination has a high dependence on the clinical experience and status of endoscopists, and the quality of endoscopic examination of endoscopists can be reduced by high-load work, and problems such as incomplete examination site coverage, incomplete detection of lesions, and incomplete image collection are easy to occur. Artificial intelligence does not have this weakness. It does not reduce its ability to work over a long period of time, and its assistance is expected to improve the detection rate of lesions

The accuracy of AI-assisted diagnostic model evaluating the intestinal readiness score

时间窗: 2 years

The quality of intestinal preparation determines the quality of colonoscopy, which is evaluated by endoscopists through the Boston score. The ai-assisted diagnostic model can also be automatically graded.The Boston bowel score is used to determine whether the bowel is adequately prepared. The Boston bowel score is divided into 4 grades (0\~3 points) from worst to cleanest. The higher the score is, the better the bowel is prepared and more conducive to colonoscopy.

次要结局

未报告次要终点

研究者

发起方
Second Affiliated Hospital, School of Medicine, Zhejiang University
申办方类型
Other
责任方
Sponsor

研究点 (1)

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