- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04912037
A Study on the Effectiveness of AI-assisted Colonoscopy in Improving the Effect of Colonoscopy Training for Trainees
A Study on the Effectiveness of Artificial Intelligence-assisted Colonoscopy in Improving the Effect of Colonoscopy Training for Trainees
Study Overview
Status
Intervention / Treatment
Detailed Description
Colonoscopy is a key technique for detecting and diagnosing lesions of the lower digestive tract.High-quality endoscopy leads to better disease outcomes.However, the demand for endoscopy is high in China, and endoscopy is in short supply.A colonoscopy is a complex technical procedure that requires training and experience for maximal accuracy and safety.Therefore, it is of great significance to improve the colonoscopy ability of novice physicians and shorten the colonoscopy training time for solving the problems such as the lack and uneven distribution of digestive endoscopists and the substandard quality of endoscopy in China.
In recent years, deep learning algorithms have been continuously developed and increasingly mature.They have been gradually applied to the medical field. Computer vision is a science that studies how to make machines "see". Through deep learning, camera and computer can replace human eyes to carry out machine vision such as target recognition, tracking and measurement.Interdisciplinary cooperation in the field of medical imaging and computer vision is also one of the research hotspots in recent years. At present, it is mainly applied to the automatic identification and detection of lesions and quality control, and has achieved good results.
Our preliminary experiments have shown that deep learning has a high accuracy in endoscopic quality monitoring, which can effectively regulate doctors' operations, reduce blind spots and improve the quality of endoscopic examination.At the same time, it can also monitor the doctor's withdrawal time in real time and improve the detection rate of adenoma.In the previous work of our research group, we have successfully developed deep learning-based colonoscopy withdraw speed monitoring and intestinal cleanliness assessment, and verified the effectiveness of the AI-assisted system(EndoAngel) in improving the quality of gastroscopy and colonoscopy in clinical trials.
Based on the above rich foundation of preliminary work, as well as the huge demand in the field of colonoscopy training,By comparing the colonoscopy operation training for novices with and without EndoAngel assistance, we plan to compare the colonoscopy learning effect of novices with and without assistance, including skill results and cognitive level, to explore whether AI can promote the improvement of the colonoscopy operation training for novices.
Study Type
Enrollment (Anticipated)
Phase
- Not Applicable
Contacts and Locations
Study Contact
- Name: Yu W Honggang, Doctor
- Phone Number: +862788041911
- Email: whdxrmyy@126.com
Study Contact Backup
- Name: Yu Honggang, Doctor
- Phone Number: +862788041911
- Email: whdxrmyy@126.com
Study Locations
-
-
Hubei
-
Wuhan, Hubei, China, 430000
- Recruiting
- Renmin Hospital of Wuhan University
-
Contact:
- Honggang Yu, Doctor
- Phone Number: +862788041911
- Email: whdxrmyy@126.com
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
Inclusion Criteria:
- Male or female ≥50 years old;
- Able to read, understand and sign informed consent
- The investigator believes that the subjects can understand the process of the clinical study, are willing and able to complete all study procedures and follow-up visits, and cooperate with the study procedures
- Patients requiring colonoscopy
Exclusion Criteria:
- Have drug or alcohol abuse or mental disorder in the last 5 years
- Pregnant or lactating women
- Patients with known multiple polyp syndrome;
- patients with known inflammatory bowel disease;
- known intestinal stenosis or space-occupying tumor;
- known colon obstruction or perforation;
- patients with a history of colorectal surgery;
- Patients with previous history of allergy to pre-used spasmolysis;
- Unable to perform biopsy and polyp removal due to coagulation disorders or oral anticoagulants;
- High risk diseases or other special conditions that the investigator considers the subject unsuitable for participation in the clinical trial.
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Diagnostic
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Double
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
---|---|
Experimental: with AI-assisted system
The novice doctors are trained in colonoscopy with an artificial intelligence assisted system that can indicate abnormal lesions and the speed of withdrawal in real time, as well as feedback on the percentage of overspeed.
|
the artificial intelligence assistance system can indicate abnormal lesions and real-time withdrawal speed, and feedback the overspeed percentage.
|
No Intervention: without AI-assisted system
The novice doctors receive routine colonoscopy training without artificial intelligence assistance system and no special tips
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Time Frame |
---|---|
CUSUM learning curve for colonoscopy (ACE scoring scale)
Time Frame: From the beginning to the end of colonoscopy training
|
From the beginning to the end of colonoscopy training
|
Average test score difference before and after training
Time Frame: From the beginning to the end of colonoscopy training
|
From the beginning to the end of colonoscopy training
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Average number of adenomas detected per patient
Time Frame: A month
|
The numerator is the total number of adenomas detected by colonoscopy, and the denominator is the total number of patients undergoing colonoscopy.
|
A month
|
The detection rate of large, small and micro polyps
Time Frame: A month
|
The numerator is the number of patients with large (≥10 mm), small (6-9 mm) and micro-small (≤5 mm) polyps detected by colonoscopy, and the denominator is the total number of patients receiving colonoscopy.
|
A month
|
The average number of large, small and micro polyps detected
Time Frame: A month
|
The numerator is the total number of large (≥10 mm), small (6-9 mm) and micro-small (≤5 mm) polyps detected by colonoscopy, and denominator is the total number of patients undergoing colonoscopy.
|
A month
|
The detection rate of large, small and micro adenomas
Time Frame: A month
|
The numerator is the number of patients with large (≥10 mm), small (6-9 mm) and micro-small (≤5 mm) adenomas detected by colonoscopy, and the denominator is the total number of patients receiving colonoscopy.
|
A month
|
The detection rate of adenoma in different sites
Time Frame: A month
|
The numerator is the number of patients with adenomas detected in the rectum, sigmoid colon, descending colon, transverse colon, ascending colon, ileocecal region and other sites during colonoscopy, and the denominator is the total number of patients receiving colonoscopy.
|
A month
|
The average number of adenomas detected in different sites
Time Frame: A month
|
The numerator is the total number of adenomas detected in the rectum, sigmoid colon, descending colon, transverse colon, ascending colon, ileocecal region and other sites during colonoscopy, and the denominator is the total number of patients undergoing colonoscopy.
|
A month
|
Detection rate of advanced adenoma
Time Frame: A month
|
The numerator is the number of patients diagnosed with advanced adenomas, and the denominator is the total number of patients undergoing colonoscopy,Advanced adenoma was defined as > 10mm adenoma, villous adenoma, tubular villous adenoma, high-grade intraepithelial neoplasia, and carcinoma.
|
A month
|
Polyp Detection Rate, PDR
Time Frame: A month
|
The numerator is the number of patients with polyps detected by colonoscopy, and the denominator is the total number of patients who underwent colonoscopy
|
A month
|
The average number of large, small and micro adenomas detected
Time Frame: A month
|
The numerator is the total number of large (≥10 mm), small (6-9 mm) and micro-small (≤5 mm) adenomas detected by colonoscopy, and denominator is the total number of patients undergoing colonoscopy.
|
A month
|
Number of missed return of the sliding endoscopy/number of successful return of the sliding endoscopy
Time Frame: A month
|
The numerator is the total number of sliding endoscopy during colonoscopy, and the denominator is the number of sliding endoscopy and successful return endoscopy during colonoscopy
|
A month
|
Real-time gut cleanliness score
Time Frame: During procedure
|
During colonoscopy, a real-time intestinal cleanliness score was given by EndoAngel based on the Boston-scale Boreal Preparation Score (BBPS).
|
During procedure
|
withdraw overspeed percentage
Time Frame: During procedure
|
The ratio of the overspeed duration to the total duration in the process of withdrawal.
|
During procedure
|
The withdraw time
Time Frame: During procedure
|
The time between colonoscopy arrival at ileocecal valve and colonoscopy exit from anus.
|
During procedure
|
Ratio of ileocecal reach
Time Frame: A month
|
For a period of time, the number of colonoscopies that failed to reach the ileocecal part accounted for the proportion of the total number of colonoscopies.
|
A month
|
Collaborators and Investigators
Investigators
- Principal Investigator: Yu w Honggang, Doctor, Renmin Hospital of Wuhan University
Study record dates
Study Major Dates
Study Start (Anticipated)
Primary Completion (Anticipated)
Study Completion (Anticipated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- EA-21-005
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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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