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临床试验/NCT04227795
NCT04227795已完成不适用

Artificial Intelligence-Assisted Real-time Detection of Missed Lesions During Colonoscopy: A Prospective Study

The University of Hong Kong1 个研究点 分布在 1 个国家目标入组 52 人开始时间: 2020年1月1日最近更新:
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
52
试验地点
1
主要终点
Adenoma miss rate

研究概览

简要总结

A prospective validation of real time deep learning artificial intelligence model for detection of missed colonic polyps

详细描述

Consecutive adult patients, age 40 or above, who were scheduled to have outpatient colonoscopy in the Queen Mary Hospital were invited to participate. Patients were excluded if they were unable to provide informed consent, considered to be unsafe for taking biopsy or polypectomy including patients with bleeding tendency and those with severe comorbid illnesses. Also, patients with history of inflammatory bowel disease, familial adenomatous polyposis, Peutz-Jeghers syndrome or other polyposis syndromes were excluded.

The primary endoscopist conducted the colonoscopic examination in the usual manner. All colonoscopy procedures were performed with high-definition colonoscopes (EVIS-EXERA 290 video system, Olympus Optical, Tokyo, Japan). The colonoscopy was first advanced to the cecum in all patients as confirmed by identification of the appendiceal orifice and ileocecal valve or by intubation of the ileum. After cecal intubation, the colonoscopy was slowly withdrawn to the rectum by the primary endoscopist. The AI real time detection was then activated with the output displayed in a different monitor and was only viewed by an independent investigator, who was an experienced endoscopist. The primary endoscopist was blinded to the AI real time detection result al.

The colon was divided into three segments during the examination: right side, transverse and left side colon, using hepatic flexure and splenic flexure as dividing landmark. All polyps were marked for size (measured with biopsy forceps), location and morphology according to the Paris classification, and then removed or biopsied for histological examination. After examination of each segment, segmental unblinding of the AI results were provided by the independent viewer. If additional polyps were detected by AI but not by the endoscopist, that segment were reexamined to look for the missed polyp. If no additional polyp was detected by the AI, the next colonic segment was examined. Missed lesions were defined as lesions identified by AI and then confirmed on reexamination by the endoscopist.

The first withdrawal time (minus the polypectomy site) was measured. The Boston Bowel Preparation Scale score (BPPS) was used for evaluation of bowel cleanliness.

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
40 Years 至 90 Years(Adult, Older Adult)
性别
All
接受健康志愿者
否

入选标准

  • •consecutive adult patients, age 40 or above, who were scheduled to have outpatient colonoscopy in the Queen Mary Hospital were invited to participate

排除标准

  • •Patients were excluded if they were unable to provide informed consent, considered to be unsafe for taking biopsy or polypectomy including patients with bleeding tendency and those with severe comorbid illnesses.
  • •Also, patients with history of inflammatory bowel disease, familial adenomatous polyposis, Peutz-Jeghers syndrome or other polyposis syndromes were excluded.

研究组 & 干预措施

Artificial intelligence-Assisted real time colonoscopy

Experimental

AI assisted real-time detection of colonic lesions

干预措施: Artificial intelligence-Assisted real time colonoscopy (Device)

结局指标

主要结局

Adenoma miss rate

时间窗: During the colonoscopy procedure

The number of patient had at least one missed adenoma

次要结局

  • Total number of missed polyps(During the colonoscopy procedure)
  • Total number of adenoma missed(During the colonoscopy procedure)
  • Colonic polyp miss rate(During the colonoscopy procedure)

研究者

申办方类型
Other
责任方
Principal Investigator
主要研究者

LEUNG Wai Keung

Clinical Professor

The University of Hong Kong

研究点 (1)

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