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OpenTrials
Completed

NCT Number: NCT04384575

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

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

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Key information

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Beijing Cancer Hospital

Beijing, Haidian, 100142, China

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

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

Treatment and study plan

blind spots

Diagnostic Test

missed part during map the entire stomach through endoscopy

Primary outcomes

  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

Sponsors and collaborators

Lead sponsor

Peking University

Other

Registry information

Important dates

Study start
2020
Primary completion
2022
Study completion
2022
First posted
May 12, 2020
Registry last updated
Nov 29, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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