Helicobacter Pylori InfectionNCT05631015
Artificial Intelligence for Determination of Gastroscopy Surveillance Intervals
An observational study of Helicobacter Pylori Infection and Atrophic Gastritis, run by Xiuli Zuo at 1 site in China.
Trial summary
The purpose of this study is to develop and validate a clinical decision support system based on automated algorithms. This system can use natural language processing to extract data from patients' endoscopic reports and pathological reports, identify patients' disease types and grades, and generate guidelines based… Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Helicobacter Pylori Infection, Atrophic Gastritis, Intestinal Metaplasia
- What is being tested?
- AI recongnize disease and generate recommendations
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2012to 31 Dec 2023
- 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
- Adult, Older adult
Study population: patients who came to Qilu Hospital of Shandong University and received endoscopy examination but not therapeutic endoscopy
Who may be able to join
- Patients aged 18 - 80 years
- Patients underwent endoscopic examination
Who cannot join
- Patients with the contraindications to endoscopic examination
- Patients with imcomplete examination information
- Patients undergo endoscopy for therapy
- Patients have history of upper gastrointestinal surgery
- Patients with duodenal or Laryngeal neoplasms
- Patients with gastrointestinal submucosal tumor
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Development and Validation of Gastroscopy Surveillance Recommendations Based on Natural Language Processing for Patients With Gastric Cancer and Precancerous Diseases
The purpose of this study is to develop and validate a clinical decision support system based on automated algorithms. This system can use natural language processing to extract data from patients' endoscopic reports and pathological reports, identify patients' disease types and grades, and generate guidelines based follow-up or treatment recommendations
- Conditions
- Helicobacter Pylori Infection, Atrophic Gastritis, Intestinal Metaplasia, Low Grade Intraepithelial Neoplasia, High Grade Intraepithelial Neoplasia, Early Gastric Cancer
- Being tested
- AI recongnize disease and generate recommendations
- Medical subject headings
- Gastritis, Atrophic, Stomach Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI recongnize disease and generate recommendationsOther
According the endoscopic reports and pathological reports, the decision support system recognise patients' disease types and grades, and generate guidelines based survilliance or treatment recommendations.
| Study group | Role | What they receive |
|---|---|---|
| Artificial Intelligence support decision group | According the endoscopic reports and pathological reports, the decision support system recognise patients' disease types and grades, and generate guidelines based survilliance or treatment recommendations. Receives: Other: AI recongnize disease and generate recommendations |
What is being measured
The primary outcome is the main question the study is designed to answer.
The diagnostic accuracy of gastric diseases with deep learning algorithm
The diagnostic accuracy of gastric diseases with deep learning algorithm
Measured: 12 month
The accuracy of recommentions for different disease with deep learning algorithm
The accuracy of recommentions for different disease with deep learning algorithm
Measured: 12 month
Secondary outcomes (5)
The diagnostic sensitivity of gastric diseases with deep learning algorithm
The diagnostic sensitivity of gastric diseases with deep learning algorithm
Measured: 12 month
The diagnostic specificity of gastric diseases with deep learning algorithm
The diagnostic specificity of gastric diseases with deep learning algorithm
Measured: 12 month
The diagnostic positive predictive value of gastric diseases with deep learning algorithm
The diagnostic positive predictive valu of gastric diseases with deep learning algorithm
Measured: 12 month
The diagnostic negative predictive value of gastric diseases with deep learning algorithm
The diagnostic negative predictive value of gastric diseases with deep learning algorithm
Measured: 12 month
The F-score of gastric diseases with deep learning algorithm
The F-score of gastric diseases with deep learning algorithm
Measured: 12 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Nov 2022
- Study start1 Jan 2012
- Primary completion31 Oct 2022
- Study completion31 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Helicobacter Pylori InfectionStudies 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
- Retrospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Xiuli Zuo
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Xiuli Zuo, Shandong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05631015
- Sponsor's protocol ID
- 2022-SDU-QILU-G008
- Status last verified
- Nov 2022
- First submitted
- 19 Nov 2022
- Last updated
- 30 Nov 2022
- 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 30 Nov 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of Gastroscopy Surveillance Recommendations Based on Natural Language Processing for Patients With Gastric Cancer and Precancerous Diseases
- ClinicalTrials.gov ID
- NCT05631015
- Acronym / sponsor ID
- 2022-SDU-QILU-G008
- Conditions
- Helicobacter Pylori Infection, Atrophic Gastritis, Intestinal Metaplasia, Low Grade Intraepithelial Neoplasia, High Grade Intraepithelial Neoplasia, Early Gastric Cancer
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Xiuli ZuoUniversity, hospital or other
- Study start
- 1 Jan 2012 (actual)
- Primary completion
- 31 Oct 2022 (actual)
- Study completion
- 31 Dec 2023 (estimated)
- First posted
- 30 Nov 2022 (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.
The purpose of this study is to develop and validate a clinical decision support system based on automated algorithms. This system can use natural language processing to extract data from patients' endoscopic reports and pathological reports, identify patients' disease types and grades, and generate guidelines based follow-up or treatment recommendations
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence support decision group | – | Other: AI recongnize disease and generate recommendationsAccording the endoscopic reports and pathological reports, the decision support system recognise patients' disease types and grades, and generate guidelines based survilliance or treatment recommendations. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- patients who came to Qilu Hospital of Shandong University and received endoscopy examination but not therapeutic endoscopy
Key inclusion criteria (2 listed)
- Patients aged 18 - 80 years
- Patients underwent endoscopic examination
Key exclusion criteria (6 listed)
- Patients with the contraindications to endoscopic examination
- Patients with imcomplete examination information
- Patients undergo endoscopy for therapy
- Patients have history of upper gastrointestinal surgery
- Patients with duodenal or Laryngeal neoplasms
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 |
|---|---|---|
| AI recongnize disease and generate recommendationsAccording the endoscopic reports and pathological reports, the decision support system recognise patients' disease types and grades, and generate guidelines based survilliance or treatment recommendations. | Other | Artificial Intelligence support decision group |
Primary outcome
The diagnostic accuracy of gastric diseases with deep learning algorithm
Time frame: 12 month
The diagnostic accuracy of gastric diseases with deep learning algorithm
The accuracy of recommentions for different disease with deep learning algorithm
Time frame: 12 month
The accuracy of recommentions for different disease with deep learning algorithm
5 secondary outcomes
The diagnostic sensitivity of gastric diseases with deep learning algorithm
Time frame: 12 month
The diagnostic specificity of gastric diseases with deep learning algorithm
Time frame: 12 month
The diagnostic positive predictive value of gastric diseases with deep learning algorithm
Time frame: 12 month
The diagnostic negative predictive value of gastric diseases with deep learning algorithm
Time frame: 12 month
The F-score of gastric diseases with deep learning algorithm
Time frame: 12 month
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Nov 2022.
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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.
The record lists no contact. Use the official record to reach the sponsor.
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 Helicobacter Pylori Infection and Atrophic Gastritis, run by Xiuli Zuo 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 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 30 Nov 2022. 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?
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?
The diagnostic accuracy of gastric diseases with deep learning algorithm (time frame: 12 month). The study lists 2 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 30 Nov 2022.