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Application of Artificial Intelligence for Early Diagnosis of Gastric Cancer During Optical Enhancement Magnifying Endoscopy

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ClinicalTrials.gov Identifier: NCT04563416
Recruitment Status : Recruiting
First Posted : September 24, 2020
Last Update Posted : September 24, 2020
Sponsor:
Information provided by (Responsible Party):
Yanqing Li, Shandong University

Brief Summary:
Previous prospective randomized controlled study demonstrated higher accuracy rate of diagnosing early gastric cancers by Magnifying image-enhanced endoscopy than conventional white-light endoscopy. Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner. we developed a new computer-aided system to assist endoscopists in identifying early gastric cancers in magnifying optical enhancement images.

Condition or disease
Artificial Intelligence Optical Enhancement Endoscopy Magnifying Endoscopy

Detailed Description:

Gastric cancer is the third most common cause of cancer-associated deaths worldwide especially in Asia.Early detection and treatment would cure the disease with 5-year survival rate greater than 90%.However, the sensitivity of conventional endoscopy with white-light imaging (C-WLI) in diagnosis of early gastric cancers (EGCs) is merely 40%. Magnifying image-enhanced endoscopy (IEE) techniques such as magnifying narrow band imaging (M-NBI) have been developed and 2 RCT report that white-light imaging combine with M-NBI can increase the sensitivity to 95%. The strategy that using white-light imaging to detect the suspicious lesion and using M-IEE techniques to make a diagnosis of early gastric cancer is recommend in screening endoscopy.

Optical enhancement (OE) which is one of the M-IEE techniques was developed by HOYA Co. (Tokyo, Japan) . This technology combines digital signal processing and optical filterers to clear display of mucosal microsurface (MS) and microvessel (MV). The advantage of OE is to overcome the darkness of NBI which leads to less usefulness for detect-ability in the full extended gastrointestinal lumen.Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner, and expertise with sub-optimal inter-observer agreement is essential for the use of M-IEE.

Nowadays, Artificial intelligence (AI) using deep machine learning has made a major breakthrough in gastroenterology, which using gradient descent method and backpropagation to automatically extract specific images features. The diagnostic accuracy in identifying upper gastrointestinal cancer was 0.955 in C-WLI . Polyps can be identified in real time with 96% accuracy in screening colonoscopy. AI show an outstanding application in detection and diagnosis.

This study aims to develop a M-OE assistance model in the diagnosis of EGCs by distinguishing cancer or not.

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Study Type : Observational
Estimated Enrollment : 80 participants
Observational Model: Other
Time Perspective: Other
Official Title: Application of Artificial Intelligence for Early Diagnosis of Gastric Cancer During Optical Enhancement Magnifying Endoscopy
Actual Study Start Date : July 10, 2020
Estimated Primary Completion Date : November 30, 2020
Estimated Study Completion Date : December 30, 2020

Resource links provided by the National Library of Medicine


Group/Cohort
Patients who need undergo magnifying endoscopy



Primary Outcome Measures :
  1. the diagnosis efficiency of the computer-assist diagnosis tool [ Time Frame: 12 months ]
    the sensitivity, specificity and accuracy of the computer-assist diagnosis tool



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Consecutive patients suspected of early gastric cancer and receive optical magnifying OE endoscopy examination.
Criteria

Inclusion Criteria:

  • patients receive optical magnifying OE endoscopy examination

Exclusion Criteria:

  • Patients with advanced cancer, lymphoma,active stage of ulcer and artificial ulcer after ESD were excluded.

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): NCT04563416


Contacts
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Contact: Yanqing Li, PHD 86-531-82169236 liyanqing@sdu.edu.cn

Locations
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China, Shandong
Department of Gastroenterology, Qilu Hospital, Shandong University Recruiting
Jinan, Shandong, China, 250012
Contact: Yanqing Li, PhD. MD.    18678827666    liyanqing@sdu.edu.cn   
Sponsors and Collaborators
Shandong University
Investigators
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Principal Investigator: Yanqing Li, PHD Study Principal Investigator Qilu Hospital, Shandong University
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Responsible Party: Yanqing Li, Vice president of Qilu Hospital, Shandong University
ClinicalTrials.gov Identifier: NCT04563416    
Other Study ID Numbers: 2018SDU-QILU-3
First Posted: September 24, 2020    Key Record Dates
Last Update Posted: September 24, 2020
Last Verified: September 2020

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Stomach Neoplasms
Gastrointestinal Neoplasms
Digestive System Neoplasms
Neoplasms by Site
Neoplasms
Digestive System Diseases
Gastrointestinal Diseases
Stomach Diseases