Gastric CancerNCT04384575
Study on the Effectiveness of Gastroscope Operation Quality Control Based on Artificial Intelligence Technology
An observational study of Gastric Cancer, run by Peking University at 1 site in China.
Trial summary
This study aims to construct a real-time quality monitoring system based on artificial intelligence technology. Read more about this study
- Who is it for?
- Ages 18 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Gastric Cancer
- What is being tested?
- blind spots
- How many people
- 1,570 participants
- Where
- 1 siteChina
- Study dates
- 22 Feb 2020to 1 May 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 75 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients who meet the criteria for gastroscopy examination.
Who may be able to join
- Patiens aged 18 years or above undergoing gastroscopy；
- Be able to read, understand and sign informed consent；
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
This study aims to construct a real-time quality monitoring system based on artificial intelligence technology.
Read the 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.
- Conditions
- Gastric Cancer
- Being tested
- blind spots
- Keywords
- gastric cancer, artificial intelligence, endoscopy
- Medical subject headings
- Stomach Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
blind spotsDiagnostic test
missed part during map the entire stomach through endoscopy
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy
Calculate the accuracy of AI's judgment on images
Measured: 2020.2.22-2020.7.1
Sensitivity
number of images in which AI correctly diagnosed positive/all images with positive
Measured: 2020.2.22-2020.7.1
Specificity
number of images in which AI correctly diagnosed negative/all images negative
Measured: 2020.2.22-2020.7.1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 May 2020
- Study start22 Feb 2020
- Primary completion13 Jan 2022
- Study completion1 May 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Gastric CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,570 participants (actual)
Sponsor and investigators
- Lead sponsor
- Peking University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Peng Yuan, Peking University
- Investigators
- Qi Wu, MD., Study chair, Peking University Cancer Hospital & Institute
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04384575
- Sponsor's protocol ID
- PX2020047
- Status last verified
- Nov 2023
- First submitted
- 8 May 2020
- Last updated
- 29 Nov 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 39691834
Study at a glance
From the registry record, last updated by the sponsor on 29 Nov 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Study on the Effectiveness of Gastroscope Operation Quality Control Based on Artificial Intelligence Technology
- ClinicalTrials.gov ID
- NCT04384575
- Acronym / sponsor ID
- PX2020047
- Conditions
- Gastric Cancer
- Keywords
- gastric cancer, artificial intelligence, endoscopy
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,570 participants (actual)
- Lead sponsor
- Peking UniversityUniversity, hospital or other
- Study start
- 22 Feb 2020 (actual)
- Primary completion
- 13 Jan 2022 (actual)
- Study completion
- 1 May 2022 (actual)
- First posted
- 12 May 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aims to construct a real-time quality monitoring system based on artificial intelligence technology.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who meet the criteria for gastroscopy examination.
Key inclusion criteria (2 listed)
- Patiens aged 18 years or above undergoing gastroscopy；
- Be able to read, understand and sign informed consent；
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| blind spotsmissed part during map the entire stomach through endoscopy | Diagnostic test | – |
Primary outcome
Accuracy
Time frame: 2020.2.22-2020.7.1
Calculate the accuracy of AI's judgment on images
Sensitivity
Time frame: 2020.2.22-2020.7.1
number of images in which AI correctly diagnosed positive/all images with positive
Specificity
Time frame: 2020.2.22-2020.7.1
number of images in which AI correctly diagnosed negative/all images negative
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Nov 2023.
- Derived from this study: PubMed 39691834Yuan 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Qi Wu, MD. study chair
Peking University Cancer Hospital & Institute
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gastric Cancer, run by Peking University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 29 Nov 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy (time frame: 2020.2.22-2020.7.1). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04384575, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Nov 2023; AzTrials last refreshed its copy on 28 Oct 2026.