Esophageal NeoplasmsNCT04136236
Automatic Diagnosis of Early Esophageal Squamous Neoplasia Using pCLE With AI
An observational study of Esophageal Neoplasms and Artificial Intelligence, run by Shandong University at 1 site in China.
Trial summary
Detection and differentiation of esophageal squamous neoplasia (ESN) are of value in improving patient outcomes. Probe-based confocal laser endomicroscopy (pCLE) can diagnose ESN accurately.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
- Esophageal Neoplasms, Artificial Intelligence, Confocal Laser Endomicroscopy
- What is being tested?
- The diagnosis of Artificial Intelligence and endoscopist
- How many people
- 57 participants
- Where
- 1 siteChina
- Study dates
- 1 Aug 2019to 31 Jan 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
- 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
- advanced esophageal squamous cell carcinoma or esophageal stenosis;
- having no suspicious lesion of ESN found by WLE and IEE
- known allergy to fluorescein sodium;
- having coagulopathy or impaired renal function;
- being pregnant or breastfeeding.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Automatic Diagnosis of Early Esophageal Squamous Neoplasia Using Probe-based Confocal Laser Endomicroscopy With Artificial Intelligence
Detection and differentiation of esophageal squamous neoplasia (ESN) are of value in improving patient outcomes. Probe-based confocal laser endomicroscopy (pCLE) can diagnose ESN accurately.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
- Esophageal Neoplasms
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
Suspected esophageal mucosal lesion is observed using pCLE, endoscopist and AI will make a diagnosis independently. In addition, the endoscopist can not see the diagnosis of AI. After a washout period, nonexpert endoscopists take the second assessment with AI assistance.
| Study group | Role | What they receive |
|---|---|---|
| esophageal mucosal lesions observed by pCLE | pCLE is used to distinguish the suspected lesions detected by white light endoscopy or IEE. 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 esophageal mucosal disease on real-time pCLE examination.
Measured: 3 years
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 esophageal mucosal disease on real-time pCLE examination) between Artificial Intelligence and endoscopists.
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Oct 2019
- Study start1 Aug 2019
- Primary completion31 Jan 2023
- Study completion31 Jan 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Esophageal NeoplasmsStudies 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
- Other
- Enrolment
- 57 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
- NCT04136236
- Sponsor's protocol ID
- 2019SDU-QILU-66
- Status last verified
- Nov 2024
- First submitted
- 20 Oct 2019
- Last updated
- 19 Nov 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 19 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Automatic Diagnosis of Early Esophageal Squamous Neoplasia Using Probe-based Confocal Laser Endomicroscopy With Artificial Intelligence
- ClinicalTrials.gov ID
- NCT04136236
- Acronym / sponsor ID
- 2019SDU-QILU-66
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 57 participants (actual)
- Lead sponsor
- Shandong UniversityUniversity, hospital or other
- Study start
- 1 Aug 2019 (actual)
- Primary completion
- 31 Jan 2023 (actual)
- Study completion
- 31 Jan 2023 (actual)
- First posted
- 23 Oct 2019 (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.
Detection and differentiation of esophageal squamous neoplasia (ESN) are of value in improving patient outcomes. Probe-based confocal laser endomicroscopy (pCLE) can diagnose ESN accurately.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
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| esophageal mucosal 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 or IEE. |
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 (5 listed)
- advanced esophageal squamous cell carcinoma or esophageal stenosis;
- having no suspicious lesion of ESN found by WLE and IEE
- known allergy to fluorescein sodium;
- having coagulopathy or impaired renal function;
- being pregnant or breastfeeding.
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 endoscopistSuspected esophageal mucosal lesion is observed using pCLE, endoscopist and AI will make a diagnosis independently. In addition, the endoscopist can not see the diagnosis of AI. After a washout period, nonexpert endoscopists take the second assessment with AI assistance. | Diagnostic test | esophageal mucosal lesions observed by pCLE |
Primary outcome
The diagnosis efficiency of Artificial Intelligence
Time frame: 3 years
The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing esophageal mucosal disease on real-time pCLE examination.
1 secondary outcome
Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists
Time frame: 1 month
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 19 Nov 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 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 Esophageal Neoplasms 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 5 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 19 Nov 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?
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: 3 years). 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 19 Nov 2024.