ColonoscopyNCT05323279
Evaluate the Effects of An AI System on Colonoscopy Quality of Novice Endoscopists
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 assist novice endoscopists in performing colonoscopy and improve the quality. Read more about this study
- Who is it for?
- Ages 18 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
- 685 participants
- Where
- 1 siteChina
- Study dates
- 24 Mar 2022to 24 Nov 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Male or female ≥18 years old;
- Able to read, understand and sign an 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 a 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.
About this study
Official title: Evaluate the Effects of An Artificial Intelligence System on Colonoscopy Quality of Novice Endoscopists: A Randomized Controlled Trial
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 assist novice endoscopists in performing colonoscopy and improve the quality.
Read the detailed description
Colonoscopy is a crucial technique for detecting and diagnosing lower digestive tract lesions. The demand for endoscopy is high in China, and endoscopy is in short supply. However, a colonoscopy is a complex technical procedure that requires training and experience for maximal accuracy and safety. The ability of different endoscopists varies greatly. Novice endoscopists generally have difficulty and high risk in entering colonoscopy, requiring experts' assistance. To some extent, this wastes the novice's productivity. If investigators can arrange the working mode of experts entering and novices withdrawing endoscopy, the clinical efficiency and resource utilization rate can be significantly improved. However, investigators must consider the poor examination ability of novice endoscopists. It is reported that the detection rate of adenoma in colonoscopy performed by endoscopists with different seniority is 7.4% ~ 52.5%. If the examination ability of novice endoscopists can be improved, this concern can be eliminated.
Deep learning algorithms have been continuously developed and increasingly mature in recent years. They have been gradually applied to the medical field. Computer vision is a science that studies how to make machines to "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 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.
Investigator's preliminary experiments have shown that deep learning has 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 investigator's research group, investigators 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 and the massive demand for improving the colonoscopy ability of novices. By comparing the performance of novices and novices with EndoAngel assistance and experts in colonoscopy, investigators want to explore whether artificial intelligence can assist novices to reach the expert level in colonoscopy.
- 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 |
|---|---|---|
| novices with AI-assisted system | Experimental | The novice doctors are assisted in colonoscopy with an artificial intelligence 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 |
| experts without AI-assisted system | No intervention | The expert doctors perform routine colonoscopy without artificial intelligence assistance system and no special tips |
| novice without AI-assisted system | No intervention | The novice doctors perform routine colonoscopy 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.
Missed diagnosis rate of adenoma
The number of newly detected adenoma in the second examination divided by the total number of adenoma detected in both examinations
Measured: A month
Secondary outcomes (10)
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, villous adenoma, tubular villous adenoma, high-grade intraepithelial neoplasia, and carcinoma.
Measured: A month
Polyp Detection Rate
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 the 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
Detection rate of adenoma
The numerator is the number of patients diagnosed with adenomas, and the denominator is the total number of patients undergoing colonoscopy.
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 posted12 Apr 2022
- Study start24 Mar 2022
- Primary completion24 Oct 2022
- Study completion24 Nov 2022
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 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
- Single blind
- Who is masked
- Investigator
- Enrolment
- 685 participants (actual)
Sponsor and investigators
- Lead sponsor
- Renmin Hospital of Wuhan University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Yu Honggang, Doctor, Principal investigator, Renmin Hospital of Wuhan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05323279
- Sponsor's protocol ID
- EA-22-002
- Status last verified
- Mar 2023
- First submitted
- 1 Mar 2022
- Last updated
- 24 Mar 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Yao L, Li X, Wu Z, Wang J, Luo C, Chen B, Luo R, Zhang L, Zhang C, Tan X, Lu Z, Zhu C, Huang Y, Tan T, Liu Z, Li Y, Li S, Yu H. Effect of artificial intelligence on novice-performed colonoscopy: a multicenter randomized controlled tandem study. Gastrointest Endosc. 2024 Jan;99(1):91-99.e9. doi: 10.1016/j.gie.2023.07.044. Epub 2023 Aug 1.
PubMed 37536635
Study at a glance
From the registry record, last updated by the sponsor on 24 Mar 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluate the Effects of An Artificial Intelligence System on Colonoscopy Quality of Novice Endoscopists: A Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT05323279
- Acronym / sponsor ID
- EA-22-002
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 685 participants (actual)
- Lead sponsor
- Renmin Hospital of Wuhan UniversityUniversity, hospital or other
- Study start
- 24 Mar 2022 (actual)
- Primary completion
- 24 Oct 2022 (actual)
- Study completion
- 24 Nov 2022 (actual)
- First posted
- 12 Apr 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, 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 assist novice endoscopists in performing colonoscopy and improve the quality.
Detailed description
Colonoscopy is a crucial technique for detecting and diagnosing lower digestive tract lesions. The demand for endoscopy is high in China, and endoscopy is in short supply. However, a colonoscopy is a complex technical procedure that requires training and experience for maximal accuracy and safety. The ability of different endoscopists varies greatly. Novice endoscopists generally have difficulty and high risk in entering colonoscopy, requiring experts' assistance. To some extent, this wastes the novice's productivity. If investigators can arrange the working mode of experts entering and novices withdrawing endoscopy, the clinical efficiency and resource utilization rate can be significantly improved. However, investigators must consider the poor examination ability of novice endoscopists. It is reported that the detection rate of adenoma in colonoscopy performed by endoscopists with different seniority is 7.4% ~ 52.5%. If the examination ability of novice endoscopists can be improved, this concern can be eliminated.
Deep learning algorithms have been continuously developed and increasingly mature in recent years. They have been gradually applied to the medical field. Computer vision is a science that studies how to make machines to "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 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.
Investigator's preliminary experiments have shown that deep learning has 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 investigator's research group, investigators 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 and the massive demand for improving the colonoscopy ability of novices. By comparing the performance of novices and novices with EndoAngel assistance and experts in colonoscopy, investigators want to explore whether artificial intelligence can assist novices to reach the expert level in colonoscopy.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (investigator)
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| novices with AI-assisted system | Experimental | Device: artificial intelligence assistance systemThe novice doctors are assisted in colonoscopy with an artificial intelligence system that can indicate abnormal lesions and the speed of withdrawal in real-time, as well as feedback on the percentage of overspeed. |
| experts without AI-assisted system | No intervention | –The expert doctors perform routine colonoscopy without artificial intelligence assistance system and no special tips |
| novice without AI-assisted system | No intervention | –The novice doctors perform routine colonoscopy without artificial intelligence assistance system and no special tips |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Male or female ≥18 years old;
- Able to read, understand and sign an 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 | novices with AI-assisted system |
Primary outcome
Missed diagnosis rate of adenoma
Time frame: A month
The number of newly detected adenoma in the second examination divided by the total number of adenoma detected in both examinations
10 secondary outcomes
Detection rate of advanced adenoma
Time frame: A month
Polyp Detection Rate
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
Detection rate of adenoma
Time frame: A month
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Mar 2023.
- Derived from this study: PubMed 37536635Yao L, Li X, Wu Z, Wang J, Luo C, Chen B, Luo R, Zhang L, Zhang C, Tan X, Lu Z, Zhu C, Huang Y, Tan T, Liu Z, Li Y, Li S, Yu H. Effect of artificial intelligence on novice-performed colonoscopy: a multicenter randomized controlled tandem study. Gastrointest Endosc. 2024 Jan;99(1):91-99.e9. doi: 10.1016/j.gie.2023.07.044. Epub 2023 Aug 1.
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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 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: finished, as of the sponsor's last update on 24 Mar 2023. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Missed diagnosis rate of adenoma (time frame: A month). 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 NCT05323279, 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 24 Mar 2023.