Colon AdenomaNCT04227795
AI-assisted Detection of Missed Colonic Polyps
A study testing Artificial intelligence-Assisted real time colonoscopy in people with Colon Adenoma and Colonic Polyp, run by The University of Hong Kong at 1 site in Hong Kong.
Trial summary
A prospective validation of real time deep learning artificial intelligence model for detection of missed colonic polyps Read more about this study
- Who is it for?
- Ages 40 to 90 years
- Conditions the study is for
- Colon Adenoma, Colonic Polyp, Colon Cancer
- What is being tested?
- Artificial intelligence-Assisted real time colonoscopy
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 52 participants
- Where
- 1 siteHong Kong
- Study dates
- 1 Jan 2020to 1 Mar 2020
- 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 40 to 90 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- consecutive adult patients, age 40 or above, who were scheduled to have outpatient colonoscopy in the Queen Mary Hospital were invited to participate
Who cannot join
- Patients were excluded if they were unable to provide informed consent, considered to be unsafe for taking biopsy or polypectomy including patients with bleeding tendency and those with severe comorbid illnesses.
- Also, patients with history of inflammatory bowel disease, familial adenomatous polyposis, Peutz-Jeghers syndrome or other polyposis syndromes were excluded.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Artificial Intelligence-Assisted Real-time Detection of Missed Lesions During Colonoscopy: A Prospective Study
A prospective validation of real time deep learning artificial intelligence model for detection of missed colonic polyps
Read the detailed description
Consecutive adult patients, age 40 or above, who were scheduled to have outpatient colonoscopy in the Queen Mary Hospital were invited to participate. Patients were excluded if they were unable to provide informed consent, considered to be unsafe for taking biopsy or polypectomy including patients with bleeding tendency and those with severe comorbid illnesses. Also, patients with history of inflammatory bowel disease, familial adenomatous polyposis, Peutz-Jeghers syndrome or other polyposis syndromes were excluded.
The primary endoscopist conducted the colonoscopic examination in the usual manner. All colonoscopy procedures were performed with high-definition colonoscopes (EVIS-EXERA 290 video system, Olympus Optical, Tokyo, Japan). The colonoscopy was first advanced to the cecum in all patients as confirmed by identification of the appendiceal orifice and ileocecal valve or by intubation of the ileum. After cecal intubation, the colonoscopy was slowly withdrawn to the rectum by the primary endoscopist. The AI real time detection was then activated with the output displayed in a different monitor and was only viewed by an independent investigator, who was an experienced endoscopist. The primary endoscopist was blinded to the AI real time detection result al.
The colon was divided into three segments during the examination: right side, transverse and left side colon, using hepatic flexure and splenic flexure as dividing landmark. All polyps were marked for size (measured with biopsy forceps), location and morphology according to the Paris classification, and then removed or biopsied for histological examination. After examination of each segment, segmental unblinding of the AI results were provided by the independent viewer. If additional polyps were detected by AI but not by the endoscopist, that segment were reexamined to look for the missed polyp. If no additional polyp was detected by the AI, the next colonic segment was examined. Missed lesions were defined as lesions identified by AI and then confirmed on reexamination by the endoscopist.
The first withdrawal time (minus the polypectomy site) was measured. The Boston Bowel Preparation Scale score (BPPS) was used for evaluation of bowel cleanliness.
- Conditions
- Colon Adenoma, Colonic Polyp, Colon Cancer
- Being tested
- Artificial intelligence-Assisted real time colonoscopy
- Keywords
- Artificial intelligence, Colonoscopy, Deep learning, Detection, Computer-aided
- Medical subject headings
- Colonic Polyps, Colonic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence-Assisted real time colonoscopyDevice
The colonoscopy was performed under artificial intelligence assistance
| Study group | Role | What they receive |
|---|---|---|
| Artificial intelligence-Assisted real time colonoscopy | Experimental | AI assisted real-time detection of colonic lesions Receives: Device: Artificial intelligence-Assisted real time colonoscopy |
What is being measured
The primary outcome is the main question the study is designed to answer.
Adenoma miss rate
The number of patient had at least one missed adenoma
Measured: During the colonoscopy procedure
Secondary outcomes (3)
Total number of adenoma missed
The total number of missed polyps for all subjects
Measured: During the colonoscopy procedure
Colonic polyp miss rate
The number of patient had at least one missed adenoma
Measured: During the colonoscopy procedure
Total number of missed polyps
The total number of missed polyps for all subjects
Measured: During the colonoscopy procedure
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Jan 2020
- Study start1 Jan 2020
- Primary completion1 Feb 2020
- Study completion1 Mar 2020
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 Colon AdenomaStudies of the same condition, open and under way.
- More trials run by The University of Hong KongThe sponsor's other registered studies.
- 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
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 52 participants (actual)
Sponsor and investigators
- Lead sponsor
- The University of Hong Kong
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, LEUNG Wai Keung, The University of Hong Kong
- Investigators
- Ka Luen, Thomas Lui, Principal investigator, Queen Mary Hospital, the University of Hong Kong
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04227795
- Sponsor's protocol ID
- UW 19-309
- Status last verified
- Mar 2020
- First submitted
- 10 Jan 2020
- Last updated
- 4 Mar 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Lui TKL, Hui CKY, Tsui VWM, Cheung KS, Ko MKL, Foo DCC, Mak LY, Yeung CK, Lui TH, Wong SY, Leung WK. New insights on missed colonic lesions during colonoscopy through artificial intelligence-assisted real-time detection (with video). Gastrointest Endosc. 2021 Jan;93(1):193-200.e1. doi: 10.1016/j.gie.2020.04.066. Epub 2020 May 4.
PubMed 32376335
Study at a glance
From the registry record, last updated by the sponsor on 4 Mar 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Assisted Real-time Detection of Missed Lesions During Colonoscopy: A Prospective Study
- ClinicalTrials.gov ID
- NCT04227795
- Acronym / sponsor ID
- UW 19-309
- Conditions
- Colon Adenoma, Colonic Polyp, Colon Cancer
- Keywords
- Artificial intelligence, Colonoscopy, Deep learning, Detection, Computer-aided
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 52 participants (actual)
- Lead sponsor
- The University of Hong KongUniversity, hospital or other
- Study start
- 1 Jan 2020 (actual)
- Primary completion
- 1 Feb 2020 (actual)
- Study completion
- 1 Mar 2020 (actual)
- First posted
- 14 Jan 2020 (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.
A prospective validation of real time deep learning artificial intelligence model for detection of missed colonic polyps
Detailed description
Consecutive adult patients, age 40 or above, who were scheduled to have outpatient colonoscopy in the Queen Mary Hospital were invited to participate. Patients were excluded if they were unable to provide informed consent, considered to be unsafe for taking biopsy or polypectomy including patients with bleeding tendency and those with severe comorbid illnesses. Also, patients with history of inflammatory bowel disease, familial adenomatous polyposis, Peutz-Jeghers syndrome or other polyposis syndromes were excluded.
The primary endoscopist conducted the colonoscopic examination in the usual manner. All colonoscopy procedures were performed with high-definition colonoscopes (EVIS-EXERA 290 video system, Olympus Optical, Tokyo, Japan). The colonoscopy was first advanced to the cecum in all patients as confirmed by identification of the appendiceal orifice and ileocecal valve or by intubation of the ileum. After cecal intubation, the colonoscopy was slowly withdrawn to the rectum by the primary endoscopist. The AI real time detection was then activated with the output displayed in a different monitor and was only viewed by an independent investigator, who was an experienced endoscopist. The primary endoscopist was blinded to the AI real time detection result al.
The colon was divided into three segments during the examination: right side, transverse and left side colon, using hepatic flexure and splenic flexure as dividing landmark. All polyps were marked for size (measured with biopsy forceps), location and morphology according to the Paris classification, and then removed or biopsied for histological examination. After examination of each segment, segmental unblinding of the AI results were provided by the independent viewer. If additional polyps were detected by AI but not by the endoscopist, that segment were reexamined to look for the missed polyp. If no additional polyp was detected by the AI, the next colonic segment was examined. Missed lesions were defined as lesions identified by AI and then confirmed on reexamination by the endoscopist.
The first withdrawal time (minus the polypectomy site) was measured. The Boston Bowel Preparation Scale score (BPPS) was used for evaluation of bowel cleanliness.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Artificial intelligence-Assisted real time colonoscopy | Experimental | Device: Artificial intelligence-Assisted real time colonoscopyAI assisted real-time detection of colonic lesions |
Who may qualify
- Ages
- 40 to 90 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- consecutive adult patients, age 40 or above, who were scheduled to have outpatient colonoscopy in the Queen Mary Hospital were invited to participate
Key exclusion criteria (2 listed)
- Patients were excluded if they were unable to provide informed consent, considered to be unsafe for taking biopsy or polypectomy including patients with bleeding tendency and those with severe comorbid illnesses.
- Also, patients with history of inflammatory bowel disease, familial adenomatous polyposis, Peutz-Jeghers syndrome or other polyposis syndromes were excluded.
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-Assisted real time colonoscopyThe colonoscopy was performed under artificial intelligence assistance | Device | Artificial intelligence-Assisted real time colonoscopy |
Primary outcome
Adenoma miss rate
Time frame: During the colonoscopy procedure
The number of patient had at least one missed adenoma
3 secondary outcomes
Total number of adenoma missed
Time frame: During the colonoscopy procedure
Colonic polyp miss rate
Time frame: During the colonoscopy procedure
Total number of missed polyps
Time frame: During the colonoscopy procedure
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 4 Mar 2020.
- Derived from this study: PubMed 32376335Lui TKL, Hui CKY, Tsui VWM, Cheung KS, Ko MKL, Foo DCC, Mak LY, Yeung CK, Lui TH, Wong SY, Leung WK. New insights on missed colonic lesions during colonoscopy through artificial intelligence-assisted real-time detection (with video). Gastrointest Endosc. 2021 Jan;93(1):193-200.e1. doi: 10.1016/j.gie.2020.04.066. Epub 2020 May 4.
- 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.
Ka Luen, Thomas Lui principal investigator
Queen Mary Hospital, the University of Hong Kong
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-Assisted real time colonoscopy in people with Colon Adenoma and Colonic Polyp, run by The University of Hong Kong at 1 site in Hong Kong.
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 1 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: finished, as of the sponsor's last update on 4 Mar 2020. 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 Hong Kong. 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?
Adenoma miss rate (time frame: During the colonoscopy procedure). 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 NCT04227795, 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 4 Mar 2020.