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Artificial Intelligence Identifying Polyps in Real-world Colonoscopy

2018年12月14日 更新者:Zhaoshen Li

Validating the Performance of Artificial Intelligence in Identifying Polyps in Real-world Colonoscopy

Recently, artificial intelligence (AI) assisted image recognition has made remarkable breakthroughs in various medical fields with the developing of deep learning and conventional neural networks (CNNs). However, all current AI assisted-diagnosis systems (ADSs) were established and validated on endoscopic images or selected videos, while its actual assisted-diagnosis performance in real-world colonoscopy is up to now unknown. Therefore, we validated the performance of an ADS in real-world colonoscopy, which is based on deep learning algorithm and CNNs, trained and tested in multicenter datasets of 20 endoscopy centers.

研究概览

详细说明

The ADS were established in changhai digestive endoscopy center to assess its efficacy in clinical practice. The ADS automatically initiated once the ileocecal valve was pictured by the colonoscopist or the colonoscopist recorded any image of colon during the insertion. When colonoscopists withdrew the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the ADS, which made it feasible to identify and classify lesions in real time. Colonoscopists were invited to respond if they doubted potential polyps in the screen, and the ADS also made a voice when identifying potential polyps, followed by repeatedly inspecting to confirm the existence of lesions. The voice of ADS could be real-time heard by colonoscopists, while the screen of ADS was placed right behind colonoscopists, where polyps identified by ADS could be seen after the colonoscopists' turning but not simultaneously. The lesion detection by ADS or colonoscopists were determined as follow: A. polyps only identified by ADS, which was considered to be missed by colonoscopists: polyps were reported by the ADS and the colonoscopists did not know the location of polyps without reminder of the ADS until the polyps disappeared from the view; B. polyps first identified by ADS: polyps were first reported by the ADS and the colonoscopists also later knew the location of polyps by themselves; C. polyps simultaneously identified by the ADS and colonoscopists: the time of reporting polyps was closely synchronal (within 1 second); D. polyps first reported by colonoscopists: polyps were first reported by the colonoscopists and the ADS also later identified the location of polyps before the colonoscopists unfolded and pictured the polyps; E. polyps only reported by colonoscopists, which was considered to be missed by the ADS: polyps were reported by the colonoscopists and the ADS did not identify the location of polyps until colonoscopists unfolded and pictured the polyps. Besides, the false-positives of real-world ADS were also reported with potential causes analyzed by colonoscopists.

研究类型

观察性的

注册 (实际的)

209

联系人和位置

本节提供了进行研究的人员的详细联系信息,以及有关进行该研究的地点的信息。

学习地点

      • Shanghai、中国、200433
        • Changhai Hospital, Second Military Medical University
      • Shanghai、中国、200433
        • Changhai Hospital

参与标准

研究人员寻找符合特定描述的人,称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

18年 至 75年 (成人、年长者)

接受健康志愿者

不

有资格学习的性别

全部

取样方法

非概率样本

研究人群

consecutive outpatient who recieved colonoscopy

描述

Inclusion Criteria:

  • patients receiving screening colonoscopy
  • patients receiving surveillance colonoscopy
  • patients receiving diagnostic colonoscopy

Exclusion Criteria:

  • patients with declined consent
  • patients with poor bowel preparation
  • patients with failed cecal intubation
  • patients with colonic resection
  • patients with inflammatory bowel diseases
  • patients with polyposis

学习计划

本节提供研究计划的详细信息,包括研究的设计方式和研究的衡量标准。

研究是如何设计的?

设计细节

  • 观测模型:仅案例
  • 时间观点:预期

队列和干预

团体/队列
干预/治疗
colonoscopy withdrawal with the ADS monitoring
The ADS automatically initiated once the ileocecal valve was pictured by the colonoscopist or the colonoscopist recorded any image of colon during the insertion. When colonoscopists withdrew the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the ADS, which made it feasible to identify and classify lesions in real time.
During the testing of trained ADS, when the system doubts colonic lesions from the input data of the test images, a rectangular frame was displayed in the endoscopic image to surround the lesion. If the system confirmed it as the colonic lesions, a sound of reminder will be played and the types of lesions (non-adenomatous polyps, adenomatous polyps and colorectal cancers) will be classified by the system. We adopted several standards to define the identification and classification of colonic lesions: 1) when the system identified and confirmed any lesion in the images of no polyps or cancers, the results were judged to be false-positive. 2) when the system both confirmed and correctly localized the lesions in images (IoU > 0.3), the results were judged to be true-positive. 3) when the system did not confirm or correctly localize the lesions, the results were judged as false-negative. 4) when system confirmed no lesions in the normal images, the results were judged to be true-negative.

研究衡量的是什么?

主要结果指标

结果测量
措施说明
大体时间
sensitivity of the ADS in identifying polyps
大体时间:1 hour
Polyps that were only reported by colonoscopists were considered to be missed by the ADS (polyps were reported by the colonoscopists and the ADS did not identify the location of polyps until colonoscopists unfolded and pictured the polyps.)
1 hour

次要结果测量

结果测量
措施说明
大体时间
false positves of the ADS per colonoscopy withdrawal
大体时间:1 hour
when the system identified and confirmed any lesion in the images with no polyps or cancers appearing, the results were judged to be false-positive.
1 hour

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

赞助

出版物和有用的链接

负责输入研究信息的人员自愿提供这些出版物。这些可能与研究有关。

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查,以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (实际的)

2018年11月1日

初级完成 (实际的)

2018年12月10日

研究完成 (实际的)

2018年12月10日

研究注册日期

首次提交

2018年11月30日

首先提交符合 QC 标准的

2018年11月30日

首次发布 (实际的)

2018年12月3日

研究记录更新

最后更新发布 (实际的)

2018年12月17日

上次提交的符合 QC 标准的更新

2018年12月14日

最后验证

2018年12月1日

更多信息

与本研究相关的术语

其他相关的 MeSH 术语

其他研究编号

  • AI-1

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

在美国制造并从美国出口的产品

不

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