Probe-based Confocal Laser EndomicroscopyNCT03787784
Automatic Classification of Colorectal Polyps Using Probe-based Endomicroscopy With Artificial Intelligence
A study testing AI presentation in people with Probe-based Confocal Laser Endomicroscopy 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 gastrointestinal mucosa during ongoing endoscopy examination. It can predict the classification of Colorectal Polyps accurately. Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Probe-based Confocal Laser Endomicroscopy, Artificial Intelligence, Colorectal Polyps
- What is being tested?
- AI presentation
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 1 May 2018to 30 Mar 2019
- Phase
- Interventional
- 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
- Healthy volunteers accepted
- Adult, Older adult
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, 1 recruiting
- Recruiting
Endoscopic unit of Qilu Hospital Shandong University
Jinan, Shandong, 250001
Yanqing Li, PhD,MD
About this study
Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables real-time histological evaluation of gastrointestinal mucosa during ongoing endoscopy examination. It can predict the classification of Colorectal Polyps accurately. However this requires much experience, which limits the application of pCLE. The investigators designed a computer program using deep neural networks to differentiate hyperplastic from neoplastic polyps automatically in pCLE examination.
- Being tested
- AI presentation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI presentationOther
Automatic diagnosis information of AI is visible to endoscopist
| Study group | Role | What they receive |
|---|---|---|
| AI visible group | Experimental | Not described Receives: Other: AI presentation |
| AI invisible group | No intervention | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
The accuracy of classifying colorectal Polyps using Probe-based endomicroscopy with deep neural networks
The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing Colorectal Polyps on real-time pCLE examination.
Measured: 4 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 Colorectal Polyps on real-time pCLE examination) between Artificial Intelligence and endoscopists.
Measured: 3 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Dec 2018
- Study start1 May 2018
- Primary completion30 Jan 2019 (estimated)
- Study completion30 Mar 2019 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Probe-based Confocal Laser EndomicroscopyStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind
- Who is masked
- Participant, Investigator, Outcomes assessor
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shandong University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yanqing Li, Shandong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03787784
- Sponsor's protocol ID
- 2018SDU-QILU-8
- Status last verified
- Sep 2018
- First submitted
- 21 Dec 2018
- Last updated
- 26 Dec 2018
- 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 26 Dec 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Automatic Classification of Colorectal Polyps Using Probe-based Endomicroscopy With Artificial Intelligence
- ClinicalTrials.gov ID
- NCT03787784
- Acronym / sponsor ID
- 2018SDU-QILU-8
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Shandong UniversityUniversity, hospital or other
- Study start
- 1 May 2018 (actual)
- Primary completion
- 30 Jan 2019 (estimated)
- Study completion
- 30 Mar 2019 (estimated)
- First posted
- 26 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 gastrointestinal mucosa during ongoing endoscopy examination. It can predict the classification of Colorectal Polyps accurately. However this requires much experience, which limits the application of pCLE. The investigators designed a computer program using deep neural networks to differentiate hyperplastic from neoplastic polyps automatically in pCLE examination.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind (participant, investigator, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI visible group | Experimental | Other: AI presentation |
| AI invisible group | No intervention | – |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- aged between 18 and 80; agree to give written informed consent.
Key exclusion criteria (1 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 |
|---|---|---|
| AI presentationAutomatic diagnosis information of AI is visible to endoscopist | Other | AI visible group |
Primary outcome
The accuracy of classifying colorectal Polyps using Probe-based endomicroscopy with deep neural networks
Time frame: 4 months
The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing Colorectal Polyps on real-time pCLE examination.
1 secondary outcome
Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists
Time frame: 3 month
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Dec 2018.
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI presentation in people with Probe-based Confocal Laser Endomicroscopy 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 1 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 26 Dec 2018. 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 Yangqing Li, PHD.MD. (053182169385, liyanqing@sdu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The accuracy of classifying colorectal Polyps using Probe-based endomicroscopy with deep neural networks (time frame: 4 months). 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 26 Dec 2018.