- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04071678
A Randomized Controlled Multicenter Study of Artificial Intelligence Assisted Digestive Endoscopy
October 20, 2019 updated by: Second Affiliated Hospital, School of Medicine, Zhejiang University
Digestive endoscopy center of the second affiliated hospital of medical college of zhejiang university and engineers of naki medical co., ltd. in Hong Kong independently developed an ai-assisted diagnostic model of digestive endoscopy in the early stage, namely the deep learning model.The deep learning model through the early stage of the study, is able to identify lesions of digest tract.The sensitivity for the diagnosis of some diseases, such as colon polyps, is 99%.
On the one hand, this auxiliary diagnostic model can guide endoscopic examination for beginners; on the other hand, it can improve the detection rate of lesions and reduce the rate of missed diagnosis; on the other hand, the overall operating efficiency of the endoscopic center is improved, which is conducive to the quality control of endoscopic examination.
Now the AI-assisted diagnostic model has been further improved, and it is planned to carry out further clinical verification in the digestive endoscopy center of our hospital.
It is connected to the endoscopic system of our hospital and used simultaneously with the existing image-text system of endoscopy to compare the practicability, sensitivity and specificity of AI-assisted diagnosis model in the diagnosis of digestive tract diseases, and focus on the quality control of endoscopic examination.
Study Overview
Status
Unknown
Conditions
Intervention / Treatment
Detailed Description
Digestive endoscopy center of the second affiliated hospital of medical college of zhejiang university and engineers of naki medical co., ltd. in Hong Kong independently developed an ai-assisted diagnostic model of digestive endoscopy in the early stage, namely the deep learning model。The deep learning model through the early stage of the study, is able to identify lesions of colon polyps, colorectal cancer, colorectal apophysis lesions, colonic diverticulum, ulcerative colitis, gastric ulcer, gastric polyps, submucosal uplift, reflux esophagitis, esophageal ulcer, esophageal polyp, esophageal erosion, esophageal ectopic gastric mucosa and esophagus varicosity, esophageal cancer, esophageal papilloma, etc.The sensitivity for the diagnosis of some diseases, such as colon polyps, is 99%.
On the one hand, this auxiliary diagnostic model can guide endoscopic examination for beginners; on the other hand, it can improve the detection rate of lesions and reduce the rate of missed diagnosis; on the other hand, the overall operating efficiency of the endoscopic center is improved, which is conducive to the quality control of endoscopic examination.
Now the AI-assisted diagnostic model has been further improved, and it is planned to carry out further clinical verification in the digestive endoscopy center of our hospital.
It is connected to the endoscopic system of our hospital and used simultaneously with the existing image-text system of endoscopy to compare the practicability, sensitivity and specificity of AI-assisted diagnosis model in the diagnosis of digestive tract diseases, and focus on the quality control of endoscopic examination.
Study Type
Observational
Enrollment (Anticipated)
3600
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Contact
- Name: Wang J An, Dr
- Phone Number: 057187783759 057187783759
- Email: HREC2013@126.com
Study Contact Backup
- Name: Cai J Ting, Dr
- Phone Number: 15267019902 15267019902
- Email: 1173920428@qq.com
Study Locations
-
-
Zhejiang
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Hangzhou, Zhejiang, China, 310000
- Recruiting
- Cai J Ting
-
Contact:
- Cai J Ting
- Phone Number: 15267019902 15267019902
- Email: 1173920428@qq.com
-
-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
18 years and older (Adult, Older Adult)
Accepts Healthy Volunteers
No
Genders Eligible for Study
All
Sampling Method
Probability Sample
Study Population
Patients who underwent painless gastroenteroscopy at the endoscopy center from September 2019 to August 2021
Description
Inclusion Criteria:
- Voluntarily sign the informed consent for this study
- Stable vital signs
- Over 18 years old
- Patients requiring painless gastroenteroscopy for various reasons
Exclusion Criteria:
- Unable or unwilling to sign a consent form, or unable to follow research procedures
- have contraindications to painless gastroenteroscopy
- Vital signs are unstable
- The lesions have been identified by gastroenteroscopy in other hospitals, which is to further confirm the patients who come to our hospital for endoscopic examination
- Endoscopic treatment, such as polypectomy, pylorus narrow dilatation and so on
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
A: Model A
Mode A was silent mode, back-to-back with endoscopic physicians to simultaneously display endoscopic images and record video, but did not interfere with the operation of endoscopic physicians.After the operation, the AI model automatically generates an endoscopy report, which is compared with the official report given by the endoscopy doctor in the endoscopy system.
If the difference is large, video verification shall be played back immediately or endoscopic examination shall be performed again before the patient wakes up
|
When the AI model alarms, check carefully to confirm the lesion
|
B: Model B
Mode B is a delayed reminder mode.
If the lesion is found during the operation, it is required to be moved to the middle of the visual field within 5 seconds.
If the lesion has been detected by the AI model (the lesion has been circled in the picture), but the doctor does not move the lesion to the middle of the visual field within 5 seconds, the AI system will give an alarm prompt
|
When the AI model alarms, check carefully to confirm the lesion
|
C: Model C
Mode C is a real-time reminder mode, which is an alarm prompt when the focus is captured in the visual field.
|
When the AI model alarms, check carefully to confirm the lesion
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Changes of detection rate of digestive tract lesions assisted by artificial intelligence gastroenteroscopy
Time Frame: 2 years
|
Endoscopic examination has a high dependence on the clinical experience and status of endoscopists, and the quality of endoscopic examination of endoscopists can be reduced by high-load work, and problems such as incomplete examination site coverage, incomplete detection of lesions, and incomplete image collection are easy to occur.
Artificial intelligence does not have this weakness.
It does not reduce its ability to work over a long period of time, and its assistance is expected to improve the detection rate of lesions
|
2 years
|
The accuracy of AI-assisted diagnostic model evaluating the intestinal readiness score
Time Frame: 2 years
|
The quality of intestinal preparation determines the quality of colonoscopy, which is evaluated by endoscopists through the Boston score.
The ai-assisted diagnostic model can also be automatically graded.The Boston bowel score is used to determine whether the bowel is adequately prepared.
The Boston bowel score is divided into 4 grades (0~3 points) from worst to cleanest.
The higher the score is, the better the bowel is prepared and more conducive to colonoscopy.
|
2 years
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Investigators
- Study Director: Cai J Ting, Dr, Second affiliated hospital of school of medicine, zhejiang university
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
August 1, 2019
Primary Completion (Anticipated)
August 1, 2021
Study Completion (Anticipated)
December 30, 2021
Study Registration Dates
First Submitted
August 26, 2019
First Submitted That Met QC Criteria
August 26, 2019
First Posted (Actual)
August 28, 2019
Study Record Updates
Last Update Posted (Actual)
October 22, 2019
Last Update Submitted That Met QC Criteria
October 20, 2019
Last Verified
August 1, 2019
More Information
Terms related to this study
Other Study ID Numbers
- 研2019-262
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
NO
IPD Plan Description
The IPD will not share to others
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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