ColonoscopyNCT04912037
A Study on the Effectiveness of AI-assisted Colonoscopy in Improving the Effect of Colonoscopy Training for Trainees
A study testing artificial intelligence assistance system in people with Colonoscopy and Artificial Intelligence, run by Renmin Hospital of Wuhan University at 1 site in China.
Trial summary
In this study,the AI-assisted system（EndoAngel）has the functions of reminding the ileocecal junction, withdrawal time, withdrawal speed, sliding lens, polyps in the field of vision, etc. These functions can improve the colonoscopy performance of novice physicians and assist the colonoscopy training。 Read more about this study
- Who is it for?
- Ages 50 years and older
- Conditions the study is for
- Colonoscopy, Artificial Intelligence, Gastrointestinal Disease
- What is being tested?
- artificial intelligence assistance system
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 385 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2021to 1 Feb 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 50 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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;
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- 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.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Renmin hospital of Wuhan University
Wuhan, Hubei, 430000
Honggang Yu, Doctor, +862788041911, whdxrmyy@126.com
About this study
Official title: A Study on the Effectiveness of Artificial Intelligence-assisted Colonoscopy in Improving the Effect of Colonoscopy Training for Trainees
In this study,the AI-assisted system（EndoAngel）has the functions of reminding the ileocecal junction, withdrawal time, withdrawal speed, sliding lens, polyps in the field of vision, etc. These functions can improve the colonoscopy performance of novice physicians and assist the colonoscopy training。
Read the 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.
- Being tested
- artificial intelligence assistance system
- Medical subject headings
- Gastrointestinal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
artificial intelligence assistance systemDevice
the artificial intelligence assistance system can indicate abnormal lesions and real-time withdrawal speed, and feedback the overspeed percentage.
| Study group | Role | What they receive |
|---|---|---|
| with AI-assisted system | Experimental | 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. Receives: Device: artificial intelligence assistance system |
| without AI-assisted system | No intervention | The novice doctors receive routine colonoscopy training without artificial intelligence assistance system and no special tips |
What is being measured
The primary outcome is the main question the study is designed to answer.
CUSUM learning curve for colonoscopy (ACE scoring scale)
Measured: From the beginning to the end of colonoscopy training
Average test score difference before and after training
Measured: From the beginning to the end of colonoscopy training
Secondary outcomes (14)
Detection rate of advanced adenoma
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.
Measured: A month
Polyp Detection Rate, PDR
The numerator is the number of patients with polyps detected by colonoscopy, and the denominator is the total number of patients who underwent colonoscopy
Measured: A month
Average number of adenomas detected per patient
The numerator is the total number of adenomas detected by colonoscopy, and the denominator is the total number of patients undergoing colonoscopy.
Measured: A month
The detection rate of large, small and micro polyps
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.
Measured: A month
The average number of large, small and micro polyps detected
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.
Measured: A month
The detection rate of large, small and micro adenomas
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.
Measured: A month
The average number of large, small and micro adenomas detected
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.
Measured: A month
The detection rate of adenoma in different sites
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.
Measured: A month
The average number of adenomas detected in different sites
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.
Measured: A month
Number of missed return of the sliding endoscopy/number of successful return of the sliding endoscopy
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
Measured: A month
Real-time gut cleanliness score
During colonoscopy, a real-time intestinal cleanliness score was given by EndoAngel based on the Boston-scale Boreal Preparation Score (BBPS).
Measured: During procedure
withdraw overspeed percentage
The ratio of the overspeed duration to the total duration in the process of withdrawal.
Measured: During procedure
The withdraw time
The time between colonoscopy arrival at ileocecal valve and colonoscopy exit from anus.
Measured: During procedure
Ratio of ileocecal reach
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.
Measured: A month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Jun 2021
- Study start1 Jun 2021 (estimated)
- Primary completion1 Jan 2022 (estimated)
- Study completion1 Feb 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for ColonoscopyStudies 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
- Double blind
- Who is masked
- Participant, Investigator
- Enrolment
- 385 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Renmin Hospital of Wuhan University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Yu w Honggang, Doctor, Principal investigator, Renmin Hospital of Wuhan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04912037
- Sponsor's protocol ID
- EA-21-005
- Status last verified
- May 2021
- First submitted
- 28 May 2021
- Last updated
- 3 Jun 2021
- 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 3 Jun 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Study on the Effectiveness of Artificial Intelligence-assisted Colonoscopy in Improving the Effect of Colonoscopy Training for Trainees
- ClinicalTrials.gov ID
- NCT04912037
- Acronym / sponsor ID
- EA-21-005
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 385 participants (estimated)
- Lead sponsor
- Renmin Hospital of Wuhan UniversityUniversity, hospital or other
- Study start
- 1 Jun 2021 (estimated)
- Primary completion
- 1 Jan 2022 (estimated)
- Study completion
- 1 Feb 2022 (estimated)
- First posted
- 3 Jun 2021 (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,the AI-assisted system（EndoAngel）has the functions of reminding the ileocecal junction, withdrawal time, withdrawal speed, sliding lens, polyps in the field of vision, etc. These functions can improve the colonoscopy performance of novice physicians and assist the colonoscopy training。
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 design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, investigator)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| with AI-assisted system | Experimental | Device: artificial intelligence assistance systemThe 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. |
| without AI-assisted system | No intervention | –The novice doctors receive routine colonoscopy training without artificial intelligence assistance system and no special tips |
Who may qualify
- Ages
- 50 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (10 listed)
- 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;
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 assistance systemthe artificial intelligence assistance system can indicate abnormal lesions and real-time withdrawal speed, and feedback the overspeed percentage. | Device | with AI-assisted system |
Primary outcome
CUSUM learning curve for colonoscopy (ACE scoring scale)
Time frame: 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
14 secondary outcomes
Detection rate of advanced adenoma
Time frame: A month
Polyp Detection Rate, PDR
Time frame: A month
Average number of adenomas detected per patient
Time frame: A month
The detection rate of large, small and micro polyps
Time frame: A month
The average number of large, small and micro polyps detected
Time frame: A month
The detection rate of large, small and micro adenomas
Time frame: A month
The average number of large, small and micro adenomas detected
Time frame: A month
The detection rate of adenoma in different sites
Time frame: A month
The average number of adenomas detected in different sites
Time frame: A month
Number of missed return of the sliding endoscopy/number of successful return of the sliding endoscopy
Time frame: A month
Real-time gut cleanliness score
Time frame: During procedure
withdraw overspeed percentage
Time frame: During procedure
The withdraw time
Time frame: During procedure
Ratio of ileocecal reach
Time frame: A month
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Jun 2021.
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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.
Yu w Honggang, Doctor principal investigator
Renmin Hospital of Wuhan University
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 assistance system in people with Colonoscopy and Artificial Intelligence, 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 4 inclusion and 10 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 3 Jun 2021. 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 Yu W Honggang, Doctor (+862788041911, whdxrmyy@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
CUSUM learning curve for colonoscopy (ACE scoring scale) (time frame: From the beginning to the end of colonoscopy training). The study lists 2 primary outcomes in all. 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 3 Jun 2021.