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
地位
完全的
条件
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
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Shanghai、中国、200433
- Changhai Hospital, Second Military Medical University
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Shanghai、中国、200433
- Changhai Hospital
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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 |
|---|---|
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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.
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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.
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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 |
|---|---|---|
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sensitivity of the ADS in identifying polyps
大体时间:1 hour
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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.)
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1 hour
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次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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false positves of the ADS per colonoscopy withdrawal
大体时间:1 hour
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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.
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1 hour
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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。
一般刊物
- Byrne MF, Chapados N, Soudan F, Oertel C, Linares Perez M, Kelly R, Iqbal N, Chandelier F, Rex DK. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. 2019 Jan;68(1):94-100. doi: 10.1136/gutjnl-2017-314547. Epub 2017 Oct 24.
- Wang Z, Meng Q, Wang S, Li Z, Bai Y, Wang D. Deep learning-based endoscopic image recognition for detection of early gastric cancer: a Chinese perspective. Gastrointest Endosc. 2018 Jul;88(1):198-199. doi: 10.1016/j.gie.2018.01.029. No abstract available.
- Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018 Oct;155(4):1069-1078.e8. doi: 10.1053/j.gastro.2018.06.037. Epub 2018 Jun 18.
- Wang Z, Zhao S, Bai Y. Artificial Intelligence as a Third Eye in Lesion Detection by Endoscopy. Clin Gastroenterol Hepatol. 2018 Sep;16(9):1537. doi: 10.1016/j.cgh.2018.04.032. No abstract available.
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
此信息直接从 clinicaltrials.gov 网站检索,没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求,请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改,我们的网站上也会自动更新.