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Artificial Intelligence Identifying Polyps in Real-world Colonoscopy

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ClinicalTrials.gov Identifier: NCT03761771
Recruitment Status : Recruiting
First Posted : December 3, 2018
Last Update Posted : December 4, 2018
Sponsor:
Information provided by (Responsible Party):
Zhaoshen Li, Changhai Hospital

Brief Summary:
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.

Condition or disease Intervention/treatment
Sensitivity of the ADS in Identifying Polyps in Real-world Colonoscopy Device: colonoscopy withdrawal with the ADS monitoring

Detailed Description:
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.

Study Type : Observational
Estimated Enrollment : 200 participants
Observational Model: Case-Only
Time Perspective: Prospective
Official Title: Validating the Performance of Artificial Intelligence in Identifying Polyps in Real-world Colonoscopy
Actual Study Start Date : November 1, 2018
Estimated Primary Completion Date : December 30, 2018
Estimated Study Completion Date : December 30, 2018

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Colonoscopy

Group/Cohort Intervention/treatment
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.
Device: colonoscopy withdrawal with the ADS monitoring
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.




Primary Outcome Measures :
  1. sensitivity of the ADS in identifying polyps [ Time Frame: 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.)


Secondary Outcome Measures :
  1. false positves of the ADS per colonoscopy withdrawal [ Time Frame: 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.



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Ages Eligible for Study:   18 Years to 75 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
consecutive outpatient who recieved colonoscopy
Criteria

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03761771


Contacts
Contact: Li Zhaoshen, MD +86-21-25070552 zhaoshenlismmu@gmail.com
Contact: Bai Yu, MD +86-13564665324 baiyu1998@hotmail.com

Locations
China
Changhai Hospital, Second Military Medical University Recruiting
Shanghai, China, 200433
Contact: zhaoshen Li, MD    86-21-81873241    zhaoshenlismmu@gmail.com   
Principal Investigator: zhaoshen Li, MD         
Changhai Hospital Recruiting
Shanghai, China, 200433
Contact: Zhaoshen Li, MD         
Sponsors and Collaborators
Zhaoshen Li

Publications of Results:
Other Publications:
Responsible Party: Zhaoshen Li, Director of Gastroenterology Dept and Digestive Endoscopy Center, Changhai Hospital
ClinicalTrials.gov Identifier: NCT03761771     History of Changes
Other Study ID Numbers: AI-1
First Posted: December 3, 2018    Key Record Dates
Last Update Posted: December 4, 2018
Last Verified: November 2018

Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Additional relevant MeSH terms:
Polyps
Pathological Conditions, Anatomical