Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 372
- 试验地点
- 5
- 主要终点
- Number of polyps detected
研究概览
简要总结
The investigators hypothesize that the clinical implementation of a deep learning AI system is an optimal tool to monitor, audit and improve the detection and classification of polyps and other anatomical landmarks during colonoscopy. The objectives of this study are to generate preliminary data to evaluate the effectiveness of AI-assisted colonoscopy on: a) the rate of detection of adenomas; b) the automatic detection of the anatomical landmarks (i.e., ileocecal valve and appendiceal orifice).
详细描述
In this trial, the investigators aim to evaluate the followings:
- the accuracy of automatic detection of important anatomical landmarks (i.e., ileocecal valve, appendiceal orifice);
- the accuracy of automatic detection of polyps/adenomas (PDR/ADR);
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 45 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Signed informed consent
- •Age 45-80 years
- •Indication to undergo a lower GI endoscopy.
排除标准
- •Coagulopathy
- •Poor general health, defined as an American Society of Anesthesiologists (ASA) physical status class >3
- •Emergency colonoscopies
- •Hospitalized patients
- •Known inflammatory bowel disease (IBD)
- •Patients currently in the emergency room
结局指标
主要结局
Number of polyps detected
时间窗: Day 1
Efficacy of AI assisted colonoscopy to detect the proportion of patients with at least 1 polyp. Polyp detection rate with an AI.
Evaluation of the automatic report of the colonoscopy quality indicators
时间窗: Day 1
Compare of the automatic detection of the ileocecal valve, appendiceal orifice, and the automatic calculation of the withdrawal time with manual detection
次要结局
未报告次要终点