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Study on the Effectiveness of Gastroscope Operation Quality Control Based on Artificial Intelligence Technology

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ClinicalTrials.gov Identifier: NCT04384575
Recruitment Status : Recruiting
First Posted : May 12, 2020
Last Update Posted : August 17, 2022
Information provided by (Responsible Party):
Peng Yuan, Peking University

Brief Summary:
This study aims to construct a real-time quality monitoring system based on artificial intelligence technology.

Condition or disease Intervention/treatment
Gastric Cancer Diagnostic Test: blind spots

Detailed Description:

Gastroscopy plays an important role in the detection and diagnosis of upper gastrointestinal diseases. It is necessary for endoscopists to operate gastroscope according to the standardized process, in order to avoid missing early lesions. However, with the rapid increase in the number of endoscopies, the workload of endoscopists increases further. High workload reduces the quality of endoscopy, resulting in incomplete observation of anatomical parts that are easy to be missed in the process of gastroscopy. There are significant differences in the operation level of different endoscopists. Therefore, carrying out artificial intelligence methods has good academic research and practical value for improving the quality of endoscopic diagnosis and treatment.

Artificial intelligence devices need to use a large number of endoscopic images, based on this, we intends to collect endoscopic image data from our hospitals for training and validation of the model.

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Study Type : Observational
Estimated Enrollment : 700 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Study on the Effectiveness of Gastroscope Operation Quality Control Based on Artificial Intelligence Technology
Actual Study Start Date : February 22, 2020
Estimated Primary Completion Date : August 15, 2022
Estimated Study Completion Date : September 1, 2022

Resource links provided by the National Library of Medicine

Intervention Details:
  • Diagnostic Test: blind spots
    missed part during map the entire stomach through endoscopy

Primary Outcome Measures :
  1. Accuracy [ Time Frame: 2020.2.22-2020.7.1 ]
    Calculate the accuracy of AI's judgment on images

  2. Sensitivity [ Time Frame: 2020.2.22-2020.7.1 ]
    number of images in which AI correctly diagnosed positive/all images with positive

  3. Specificity [ Time Frame: 2020.2.22-2020.7.1 ]
    number of images in which AI correctly diagnosed negative/all images negative

Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years to 75 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
Patients who meet the criteria for gastroscopy examination.

Inclusion Criteria:

  1. Patiens aged 18 years or above undergoing gastroscopy;
  2. Be able to read, understand and sign informed consent;

Exclusion Criteria:

  1. Patients with absolute contraindications to endoscopy examination;
  2. pregnant women;
  3. previous history of gastric surgery;
  4. the researcher considers that the subject is not suitable for clinical trial.

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): NCT04384575

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Contact: Peng Yuan, MD. +86 010-88196403 peng8264503@126.com

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China, Haidian
Beijing Cancer Hospital Recruiting
Beijing, Haidian, China, 100142
Contact: Pemg Yuan, MD.    +86 010-88196403    peng8264503@126.com   
Sponsors and Collaborators
Peking University
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Study Chair: Qi Wu, MD. Peking University Cancer Hospital & Institute
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Responsible Party: Peng Yuan, MD,PHD, Peking University
ClinicalTrials.gov Identifier: NCT04384575    
Other Study ID Numbers: PX2020047
First Posted: May 12, 2020    Key Record Dates
Last Update Posted: August 17, 2022
Last Verified: August 2022

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Peng Yuan, Peking University:
gastric cancer
artificial intelligence
Additional relevant MeSH terms:
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Stomach Neoplasms
Gastrointestinal Neoplasms
Digestive System Neoplasms
Neoplasms by Site
Digestive System Diseases
Gastrointestinal Diseases
Stomach Diseases