Artificial IntelligenceNCT05479253
AI-assisted Endoscopy Report System In Improving Reporting Quality
A study testing Artificial intelligence assistant system in people with Artificial Intelligence and Endoscopy, run by Renmin Hospital of Wuhan University at 1 site in China.
Trial summary
In this study, we proposed a prospective study about the effectiveness of artificial intelligence system for endoscopy report quality in endoscopists. The subjects would be divided into two groups. Read more about this study
- Who is it for?
- Ages 18 to 70 yearsHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Endoscopy
- What is being tested?
- Artificial intelligence assistant system
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 10 participants
- Where
- 1 siteChina
- Study dates
- 1 Nov 2021to 1 Dec 2022
- 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 70 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Males or females who are over 18 years old;
- After qualified medical education and obtained the Certificate of Chinese medical practitioner;
Who cannot join
- Doctors without qualified medical education and didn't obtaine the Certificate of Chinese medical practitioner;
- The researcher believes that the subjects are not suitable for participating in clinical trials.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Renmin Hospital of Wuhan University
Wuhan, 430060
Honggang Yu, Doctor, 02788041911, whdxrmyy@126.com
About this study
Official title: A Single-center Study of an AI-assisted Endoscopy Report System In Improving Reporting Quality
In this study, we proposed a prospective study about the effectiveness of artificial intelligence system for endoscopy report quality in endoscopists. The subjects would be divided into two groups. For the collected endoscopic videos, group A would complete the endoscopy report with the assistance of the artificial intelligence system. The artificial intelligence assistant system can automatically capture images, prompt abnormal lesions and the parts covered by the examination (the upper gastrointestinal tract is divided into 26 parts). Group B would complete the endoscopy report without special prompts. After a period of forgetting, the two groups switched, that is, group A without AI assistance and group B with AI assistance to complete the endoscopy report. Then, the completeness of the report lesion, the accuracy of the lesion location, the completeness of the lesion and the standard part in the captured images, and so on were compared with or without AI assistance.
- Conditions
- Artificial Intelligence, Endoscopy
- Being tested
- Artificial intelligence assistant system
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence assistant systemDiagnostic test
The artificial intelligence assistant system can automatically capture images, prompt abnormal lesions and the parts covered by the examination (the stomach is divided into 26 parts).
| Study group | Role | What they receive |
|---|---|---|
| with Artificial intelligence assistant system | Experimental | Endoscopists would complete the endoscopy report with the assistance of the artificial intelligence system. Receives: Diagnostic Test: Artificial intelligence assistant system |
| without Artificial intelligence assistant system | No intervention | Endoscopists would complete the endoscopy report without special prompts. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Integrity of report lesion
Report lesion integrity with or without AI-assisted. Calculation method = number of report lesions / total number of lesions x 100%
Measured: one month
Accuracy of lesion location
Accuracy of lesion location with or without AI-assisted. Calculation method = number of lesion with correct location / total number of lesions x 100%
Measured: one month
Integrity of lesion in captured images
Lesion integrity in captured images with or without AI-assisted. Calculation method = number of lesions in captured images / total number of lesions x 100%
Measured: one month
Integrity of standard part in captured images
Lesion integrity in captured images with or without AI-assisted. Calculation method = number of standard parts in captured images / the actual number of standard parts covered by the examination x 100%
Measured: one month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jul 2022
- Study start1 Nov 2021
- Primary completion1 Dec 2022 (estimated)
- Study completion1 Dec 2022 (estimated)
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 Artificial IntelligenceStudies 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
- Device feasibility
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Enrolment
- 10 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Renmin Hospital of Wuhan University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05479253
- Sponsor's protocol ID
- EA-21-010
- Status last verified
- Jul 2022
- First submitted
- 27 Jul 2022
- Last updated
- 10 Aug 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Zhang L, Lu Z, Yao L, Dong Z, Zhou W, He C, Luo R, Zhang M, Wang J, Li Y, Deng Y, Zhang C, Li X, Shang R, Xu M, Wang J, Zhao Y, Wu L, Yu H. Effect of a deep learning-based automatic upper GI endoscopic reporting system: a randomized crossover study (with video). Gastrointest Endosc. 2023 Aug;98(2):181-190.e10. doi: 10.1016/j.gie.2023.02.025. Epub 2023 Feb 25.
PubMed 36849056
Study at a glance
From the registry record, last updated by the sponsor on 10 Aug 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Single-center Study of an AI-assisted Endoscopy Report System In Improving Reporting Quality
- ClinicalTrials.gov ID
- NCT05479253
- Acronym / sponsor ID
- EA-21-010
- Conditions
- Artificial Intelligence, Endoscopy
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 10 participants (estimated)
- Lead sponsor
- Renmin Hospital of Wuhan UniversityUniversity, hospital or other
- Study start
- 1 Nov 2021 (actual)
- Primary completion
- 1 Dec 2022 (estimated)
- Study completion
- 1 Dec 2022 (estimated)
- First posted
- 29 Jul 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.
In this study, we proposed a prospective study about the effectiveness of artificial intelligence system for endoscopy report quality in endoscopists. The subjects would be divided into two groups. For the collected endoscopic videos, group A would complete the endoscopy report with the assistance of the artificial intelligence system. The artificial intelligence assistant system can automatically capture images, prompt abnormal lesions and the parts covered by the examination (the upper gastrointestinal tract is divided into 26 parts). Group B would complete the endoscopy report without special prompts. After a period of forgetting, the two groups switched, that is, group A without AI assistance and group B with AI assistance to complete the endoscopy report. Then, the completeness of the report lesion, the accuracy of the lesion location, the completeness of the lesion and the standard part in the captured images, and so on were compared with or without AI assistance.
Study design
- Primary purpose
- Device feasibility
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| with Artificial intelligence assistant system | Experimental | Diagnostic Test: Artificial intelligence assistant systemEndoscopists would complete the endoscopy report with the assistance of the artificial intelligence system. |
| without Artificial intelligence assistant system | No intervention | –Endoscopists would complete the endoscopy report without special prompts. |
Who may qualify
- Ages
- 18 to 70 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Males or females who are over 18 years old;
- After qualified medical education and obtained the Certificate of Chinese medical practitioner;
Key exclusion criteria (2 listed)
- Doctors without qualified medical education and didn't obtaine the Certificate of Chinese medical practitioner;
- The researcher believes that the subjects are not suitable for participating in clinical trials.
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 |
|---|---|---|
| Artificial intelligence assistant systemThe artificial intelligence assistant system can automatically capture images, prompt abnormal lesions and the parts covered by the examination (the stomach is divided into 26 parts). | Diagnostic test | with Artificial intelligence assistant system |
Primary outcome
Integrity of report lesion
Time frame: one month
Report lesion integrity with or without AI-assisted. Calculation method = number of report lesions / total number of lesions x 100%
Accuracy of lesion location
Time frame: one month
Accuracy of lesion location with or without AI-assisted. Calculation method = number of lesion with correct location / total number of lesions x 100%
Integrity of lesion in captured images
Time frame: one month
Lesion integrity in captured images with or without AI-assisted. Calculation method = number of lesions in captured images / total number of lesions x 100%
Integrity of standard part in captured images
Time frame: one month
Lesion integrity in captured images with or without AI-assisted. Calculation method = number of standard parts in captured images / the actual number of standard parts covered by the examination x 100%
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Aug 2022.
- Derived from this study: PubMed 36849056Zhang L, Lu Z, Yao L, Dong Z, Zhou W, He C, Luo R, Zhang M, Wang J, Li Y, Deng Y, Zhang C, Li X, Shang R, Xu M, Wang J, Zhao Y, Wu L, Yu H. Effect of a deep learning-based automatic upper GI endoscopic reporting system: a randomized crossover study (with video). Gastrointest Endosc. 2023 Aug;98(2):181-190.e10. doi: 10.1016/j.gie.2023.02.025. Epub 2023 Feb 25.
- Individual participant dataSharing planned: No
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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 Artificial intelligence assistant system in people with Artificial Intelligence and Endoscopy, run by Renmin Hospital of Wuhan 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: status not confirmed, as of the sponsor's last update on 10 Aug 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?
Contact Honggang Yu, MD (13871281899, yuhonggang1969@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Integrity of report lesion (time frame: one month). The study lists 4 primary outcomes in all. 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 NCT05479253, 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 10 Aug 2022.