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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Total Enrollment

1570 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-02-22

Study Completion Date

2022-05-01

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Gastric Cancer

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

blind spots

missed part during map the entire stomach through endoscopy

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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.

Minimum Eligible Age

18 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Peng Yuan

MD,PHD

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

Learn about the lead researchers overseeing the trial and their institutional affiliations.

Qi Wu, MD.

Role: STUDY_CHAIR

Peking University Cancer Hospital & Institute

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Beijing Cancer Hospital

Beijing, Haidian, China

Site Status

Countries

Review the countries where the study has at least one active or historical site.

China

References

Explore related publications, articles, or registry entries linked to this study.

Yuan P, Ma ZH, Yan Y, Li SJ, Wang J, Wu Q. Artificial Intelligence-Based Classification of Anatomical Sites in Esophagogastroduodenoscopy Images. Int J Gen Med. 2024 Dec 12;17:6127-6138. doi: 10.2147/IJGM.S481127. eCollection 2024.

Reference Type DERIVED

PMID: 39691834 (View on PubMed)

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

PX2020047

Identifier Type: -

Identifier Source: org_study_id