Atrophic GastritisNCT05916014
AI-assisted White Light Endoscopy to Identify the Kimura-Takemoto Classification of Atrophic Gastritis
An observational study of Atrophic Gastritis and Artificial Intelligence, run by Shandong University at 1 site in China.
Trial summary
Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Atrophic Gastritis, Artificial Intelligence, Endoscopy
- What is being tested?
- Diagnostic Test: The diagnosis of Artificial Intelligence and endosopists
- How many people
- 1,500 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2023to 31 Dec 2024
- 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 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Consecutive patients who receive the gastrointestinal endoscopy examination and screened that fulfill the eligibility criteria at Qilu Hospital,Shandong University,Linyi County People's Hospital will be enrolled into the study
Who may be able to join
- Patients aged 18-80 years who undergo the white light endoscope examination Informed consent form provided by the patient.
Who cannot join
- patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric;
- disorders who cannot participate in gastroscopy;
- Patients with progressive gastric cancer;
- low quality pictures;
- patients with previous surgical procedures on the stomach or esophageal;
- patients who refuse to sign the informed consent form;
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Department of Gastrology, QiLu Hospital, Shandong University
Shangdong, Shandong, 250012
yanqing Li, MD, PHD, 0531182169385, liyanqing@sdu.edu.cn
About this study
Official title: Artificial Intelligence-assisted White Light Endoscopy to Identify the Kimura-Takemoto Classification of Atrophic Gastritis to Achieve Gastric Cancer Risk Assessment
Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Therefore, this study aims to develop an AI model to identify the Kimura-Takemoto classification.
Read the detailed description
Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. The higher the score, the more severe the degree of atrophic gastritis. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Therefore, this study aims to develop an AI model to identify the Kimura-Takemoto classification of atrophic gastritis to achieve gastric cancer risk assessment.
- Conditions
- Atrophic Gastritis, Artificial Intelligence, Endoscopy
- Being tested
- Diagnostic Test: The diagnosis of Artificial Intelligence and endosopists
- Medical subject headings
- Gastritis, Atrophic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Diagnostic Test: The diagnosis of Artificial Intelligence and endosopistsDiagnostic test
Endosopists and AI will assess the Kimura-Takemoto classification independently when the patients is eligible.
| Study group | Role | What they receive |
|---|---|---|
| Chronic atrophic gastritis observed by white light endoscope | Get pictures from gastric antrum,gastric angle,lesser curvature of gastric body, cardia, gastric fundus, greater curvature of gastric body by white light endoscope Receives: Diagnostic Test: Diagnostic Test: The diagnosis of Artificial Intelligence and endosopists |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of AI model to diagnose the Kimura-Takemoto classification
Accuracy of AI model to diagnose the Kimura-Takemoto classification
Measured: 2 years
Sensitivity of AI model to diagnose the Kimura-Takemoto classification
Sensitivity of AI model to diagnose the Kimura-Takemoto classification
Measured: 2 years
Specificity of AI model to diagnose the Kimura-Takemoto classification
Specificity of AI model to diagnose the Kimura-Takemoto classification
Measured: 2 years
Secondary outcomes (1)
The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture
The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Jun 2023
- Study start1 Jun 2023
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Atrophic GastritisStudies 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
- Other
- Time perspective
- Prospective
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shandong University
- Sponsor type
- University, hospital or other
- Collaborators
- Linyi County People's Hospital,Dezhou,China
- Responsible party
- Principal investigator, Yanqing Li, Shandong University
- Investigators
- yanqing li, MD,PHD, Study chair, Qilu Hospital of Shandong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05916014
- Sponsor's protocol ID
- 2022SDU-QILU-123
- Status last verified
- Apr 2024
- First submitted
- 14 Jun 2023
- Last updated
- 12 Apr 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-assisted White Light Endoscopy to Identify the Kimura-Takemoto Classification of Atrophic Gastritis to Achieve Gastric Cancer Risk Assessment
- ClinicalTrials.gov ID
- NCT05916014
- Acronym / sponsor ID
- 2022SDU-QILU-123
- Conditions
- Atrophic Gastritis, Artificial Intelligence, Endoscopy
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Shandong UniversityUniversity, hospital or other
- Collaborators
- Linyi County People's Hospital,Dezhou,China
- Study start
- 1 Jun 2023 (actual)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 23 Jun 2023 (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.
Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Therefore, this study aims to develop an AI model to identify the Kimura-Takemoto classification.
Detailed description
Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. The higher the score, the more severe the degree of atrophic gastritis. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Therefore, this study aims to develop an AI model to identify the Kimura-Takemoto classification of atrophic gastritis to achieve gastric cancer risk assessment.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Chronic atrophic gastritis observed by white light endoscope | – | Diagnostic Test: Diagnostic Test: The diagnosis of Artificial Intelligence and endosopistsGet pictures from gastric antrum,gastric angle,lesser curvature of gastric body, cardia, gastric fundus, greater curvature of gastric body by white light endoscope |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Consecutive patients who receive the gastrointestinal endoscopy examination and screened that fulfill the eligibility criteria at Qilu Hospital,Shandong University,Linyi County People's Hospital will be enrolled into the study
Key inclusion criteria (1 listed)
- Patients aged 18-80 years who undergo the white light endoscope examination Informed consent form provided by the patient.
Key exclusion criteria (6 listed)
- patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric;
- disorders who cannot participate in gastroscopy;
- Patients with progressive gastric cancer;
- low quality pictures;
- patients with previous surgical procedures on the stomach or esophageal;
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 |
|---|---|---|
| Diagnostic Test: The diagnosis of Artificial Intelligence and endosopistsEndosopists and AI will assess the Kimura-Takemoto classification independently when the patients is eligible. | Diagnostic test | Chronic atrophic gastritis observed by white light endoscope |
Primary outcome
Accuracy of AI model to diagnose the Kimura-Takemoto classification
Time frame: 2 years
Accuracy of AI model to diagnose the Kimura-Takemoto classification
Sensitivity of AI model to diagnose the Kimura-Takemoto classification
Time frame: 2 years
Sensitivity of AI model to diagnose the Kimura-Takemoto classification
Specificity of AI model to diagnose the Kimura-Takemoto classification
Time frame: 2 years
Specificity of AI model to diagnose the Kimura-Takemoto classification
1 secondary outcome
The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture
Time frame: 2 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Apr 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.
yanqing li, MD,PHD study chair
Qilu Hospital of Shandong University
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 Atrophic Gastritis and Artificial Intelligence, run by Shandong 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 1 inclusion and 6 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: status not confirmed, as of the sponsor's last update on 12 Apr 2024. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact yanqing Li, MD, PHD (0531182169385, liyanqing@sdu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of AI model to diagnose the Kimura-Takemoto classification (time frame: 2 years). 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?
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 12 Apr 2024.