NCT03637712已完成不适用
Deep-Learning for Automatic Polyp Detection During Colonoscopy
适应症
干预措施
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 5
- 试验地点
- 1
- 主要终点
- Adenoma Detection Rate
研究概览
简要总结
The primary objective of this study is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine whether a combination of colonoscopy and an automatic polyp detection software is a feasible way to increase adenoma detection rate compared to standard colonoscopy.
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
- 18 Years 至 99 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients presenting for routine colonoscopy for screening and/or surveillance purposes.
- •Ability to provide written, informed consent and understand the responsibilities of trial participation
排除标准
- •People with diminished cognitive capacity.
- •The subject is pregnant or planning a pregnancy during the study period.
- •Patients undergoing diagnostic colonoscopy (e.g. as an evaluation for active GI bleed)
- •Patients with incomplete colonoscopies (those where endoscopists did not successfully intubate the cecum due to technical difficulties or poor bowel preparation)
- •Patients that have standard contraindications to colonoscopy in general (e.g. documented acute diverticulitis, fulminant colitis and known or suspected perforation).
- •Patients with inflammatory bowel disease
- •Patients with any polypoid/ulcerated lesion > 20mm concerning for invasive cancer on endoscopy.
研究组 & 干预措施
Screening Colonoscopy
Experimental
Patients undergoing standard screening or surveillance colonoscopy will be included
干预措施: Computer Algorithm (Device)
结局指标
主要结局
Adenoma Detection Rate
时间窗: 1 Day
the proportion of colonoscopic examinations performed that detect one or more polyp
次要结局
未报告次要终点
研究者
研究点 (1)
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