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临床试验/NCT04087824
NCT04087824Unknown不适用

Development and Validation of a Deep Learning Algorithm for Real-time Recognition of Colonic Segments.

Shandong University0 个研究点目标入组 60 人开始时间: 2019年9月15日最近更新:
适应症
干预措施

试验速览

阶段
不适用
发起方
入组人数
60
主要终点
The accuracy of each colonic segment real-time recognition with deep learning algorithm.

研究概览

简要总结

The purpose of this study is to develop and validate a deep learning algorithm to realize automatic recognition of colonic segments under conventional colonoscopy. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.

详细描述

Colonoscopy is recommended as a routine examination for colorectal cancer screening. Complete inspection of all colon segments is the basis of colonoscopy quality control, and furthermore improves the detection rates of small adenomas. Recently, deep learning algorithm based on central neural networks (CNN) has shown multiple potential in computer-aided detection and computer-aided diagnose of gastrointestinal lesions. However, there is still a blank in recognition of anatomic sites, which restricts the realization of AI-aided lesions detection and disease severity scoring. This study aim to train an algorithm to recognize key colonic segments, and testify the accuracy of each segments recognition as compared to endoscopic physicians.

研究设计

研究类型
Interventional
分配方式
Na
干预模型
Single Group
主要目的
Health Services Research
盲法
None

入排标准

年龄范围
18 Years 至 70 Years(Adult, Older Adult)
性别
All
接受健康志愿者
否

入选标准

  • •Patients aged 18-70 years undergoing conventional colonoscopy

排除标准

  • •Known or suspected bowel obstruction, stricture or perforation
  • •Compromised swallowing reflex or mental status
  • •Severe chronic renal failure(creatinine clearance < 30 ml/min)
  • •Severe congestive heart failure (New York Heart Association class III or IV)
  • •Uncontrolled hypertension (systolic blood pressure > 170 mm Hg, diastolic blood pressure > 100 mm Hg)
  • •Dehydration
  • •Disturbance of electrolytes
  • •Pregnancy or lactation
  • •Hemodynamically unstable
  • •Unable to give informed consent

研究组 & 干预措施

AI monitoring colonoscopy

Experimental

Patients in this group go through colonoscopy under the AI monitoring device.

干预措施: AI assisted recognition of colonic segments (Device)

结局指标

主要结局

The accuracy of each colonic segment real-time recognition with deep learning algorithm.

时间窗: 3 months.

The segmental recognition accuracy is the proportion of correctly recognized segments divided by the number of involved patients. The accuracy rate of ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum will be separately calculated.

次要结局

  • The accuracy of total colonic segments recognition with deep learning algorithm as compared to endoscopic experts group.(3 months.)

研究者

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

Xiuli Zuo

director of Qilu Hospital gastroenterology department

Shandong University

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