Gastric DiseasesNCT03784209
Automatic Real-time Diagnosis of Gastric Mucosal Disease Using pCLE With Artificial Intelligence
An observational study of Gastric Diseases and Artificial Intelligence, run by Shandong University at 1 site in China.
Trial summary
Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables real-time histological evaluation of gastric mucosal disease during ongoing endoscopy examination. However this requires much experience, which limits the application of pCLE. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Gastric Diseases, Artificial Intelligence, Confocal Laser Endomicroscopy
- What is being tested?
- The diagnosis of Artificial Intelligence and endoscopist
- How many people
- 951 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2018to 29 Sep 2021
- 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 upper gastrointestinal tract pCLE examination and screened that fulfill the eligibility criteria at Qilu Hospital, Shandong University will be enrolled into the study
Who may be able to join
- aged between 18 and 80;
- agree to give written informed consent.
Who cannot join
- Patients under conditions unsuitable for performing CLE including coagulopathy , impaired renal or hepatic function, pregnancy or breastfeeding, and known allergy to fluorescein sodium;
- Inability to provide informed consent
Trial locations and contacts
1 site in 1 country.
China1 site
Endoscopic unit of Qilu Hospital Shandong University
Jinan, Shandong, 250001
About this study
Official title: Automatic Real-time Diagnosis of Gastric Mucosal Disease Using Probe-based Confocal Laser Endomicroscopy With Artificial Intelligence
Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables real-time histological evaluation of gastric mucosal disease during ongoing endoscopy examination. However this requires much experience, which limits the application of pCLE. The investigators designed a computer-aided diagnosis program using deep neural network to make diagnosis automatically in pCLE examination and contrast its performance with endoscopists.
- Being tested
- The diagnosis of Artificial Intelligence and endoscopist
- Medical subject headings
- Stomach Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
The diagnosis of Artificial Intelligence and endoscopistDiagnostic test
When suspected lesion is observed using pCLE, endoscopist and AI will make a diagnosis independently. In addition, the endoscopist can not see the diagnosis of AI.
| Study group | Role | What they receive |
|---|---|---|
| lesions observed by pCLE | pCLE is used to distinguish the suspected lesions detected by white light endoscopy. Receives: Diagnostic Test: The diagnosis of Artificial Intelligence and endoscopist |
What is being measured
The primary outcome is the main question the study is designed to answer.
The diagnosis efficiency of Artificial Intelligence
The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing gastric mucosal disease on real-time pCLE examination.
Measured: 24 months
Secondary outcomes (1)
Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists
The secondary outcome is to compare the diagnosis efficiency (including diagnostic accuracy, sensitivity, specificity, PPV, NPV for diagnosing gastric mucosal disease on real-time pCLE examination) between Artificial Intelligence and endoscopists.
Measured: 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Dec 2018
- Study start1 Jul 2018
- Primary completion29 Sep 2021
- Study completion29 Sep 2021
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 DiseasesStudies 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
- 951 participants (actual)
Sponsor and investigators
- Lead sponsor
- Shandong University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yanqing Li, Shandong University
- Investigators
- Yanqing Li, Principal investigator, Qilu Hospital of Shandong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03784209
- Sponsor's protocol ID
- 2018SDU-QILU-12
- Status last verified
- Mar 2022
- First submitted
- 16 Dec 2018
- Last updated
- 1 Apr 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Liu G, Li G, Li Z, Shao X, Ji R, Ma T, Zhang Y, Su J, Qi Q, Guo J, He Y, Yang X, Li Y, Zuo X. Deep learning-aided optical biopsy achieves whole-chain diagnosis of Correa cascade of gastric cancer: a prospective study. BMC Med. 2025 Sep 30;23(1):527. doi: 10.1186/s12916-025-04310-9.
PubMed 41029674
Study at a glance
From the registry record, last updated by the sponsor on 1 Apr 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Automatic Real-time Diagnosis of Gastric Mucosal Disease Using Probe-based Confocal Laser Endomicroscopy With Artificial Intelligence
- ClinicalTrials.gov ID
- NCT03784209
- Acronym / sponsor ID
- 2018SDU-QILU-12
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 951 participants (actual)
- Lead sponsor
- Shandong UniversityUniversity, hospital or other
- Study start
- 1 Jul 2018 (actual)
- Primary completion
- 29 Sep 2021 (actual)
- Study completion
- 29 Sep 2021 (actual)
- First posted
- 21 Dec 2018 (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.
Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables real-time histological evaluation of gastric mucosal disease during ongoing endoscopy examination. However this requires much experience, which limits the application of pCLE. The investigators designed a computer-aided diagnosis program using deep neural network to make diagnosis automatically in pCLE examination and contrast its performance with endoscopists.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| lesions observed by pCLE | – | Diagnostic Test: The diagnosis of Artificial Intelligence and endoscopistpCLE is used to distinguish the suspected lesions detected by white light endoscopy. |
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 upper gastrointestinal tract pCLE examination and screened that fulfill the eligibility criteria at Qilu Hospital, Shandong University will be enrolled into the study
Key inclusion criteria (2 listed)
- aged between 18 and 80;
- agree to give written informed consent.
Key exclusion criteria (2 listed)
- Patients under conditions unsuitable for performing CLE including coagulopathy , impaired renal or hepatic function, pregnancy or breastfeeding, and known allergy to fluorescein sodium;
- Inability to provide informed consent
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 |
|---|---|---|
| The diagnosis of Artificial Intelligence and endoscopistWhen suspected lesion is observed using pCLE, endoscopist and AI will make a diagnosis independently. In addition, the endoscopist can not see the diagnosis of AI. | Diagnostic test | lesions observed by pCLE |
Primary outcome
The diagnosis efficiency of Artificial Intelligence
Time frame: 24 months
The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing gastric mucosal disease on real-time pCLE examination.
1 secondary outcome
Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists
Time frame: 24 months
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 1 Apr 2022.
- Derived from this study: PubMed 41029674Liu G, Li G, Li Z, Shao X, Ji R, Ma T, Zhang Y, Su J, Qi Q, Guo J, He Y, Yang X, Li Y, Zuo X. Deep learning-aided optical biopsy achieves whole-chain diagnosis of Correa cascade of gastric cancer: a prospective study. BMC Med. 2025 Sep 30;23(1):527. doi: 10.1186/s12916-025-04310-9.
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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 principal investigator
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 Gastric Diseases 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 2 inclusion and 2 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 1 Apr 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?
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?
The diagnosis efficiency of Artificial Intelligence (time frame: 24 months). 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 NCT03784209, 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 1 Apr 2022.