Compare Between Computer-assisted Colonoscopy and Standard ColonoscopyNCT03842059
Computer-aided Detection for Colonoscopy
A study testing Computer-aided detection and Standard colonoscopy in people with Compare Between Computer-assisted Colonoscopy and Standard Colonoscopy, run by Tri-Service General Hospital (TSGH).
Trial summary
We developed an artificial intelligent computer system with a deep neural network to analyze real-time video signals from the endoscopy station. This randomised controlled trial compared adenoma detection rate between computer-assisted colonoscopy and standard colonoscopy. Read more about this study
- Who is it for?
- Ages 20 years and olderHealthy volunteers can take part
- Conditions the study is for
- Compare Between Computer-assisted Colonoscopy and Standard Colonoscopy
- What is being tested?
- Computer-aided detection and Standard colonoscopy
- Type of treatment
- Medical device
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 1,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Mar 2019to 31 Dec 2021
- 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 20 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Patients aged ≥20 years, scheduled for colonoscopy for one of the following indications for colonoscopy, were invited to participate in this study: polyp surveillance, changed bowel habits and/or bloody stools, bowel complaints, a positive family history for CRC, a positive FOBT, abdominal pain, diarrhoea, post-polypectomy surveillance.
Who cannot join
- We excluded patients from this study if: (1) they had known colonic neoplasia or inflammatory or other significant colonic disease, such as patients specifically presenting for polypectomy; (2) there was open bleeding or they were receiving an emergency colonoscopy; (3) they had previously previous colonic resection; (4) they were in poor general condition (more than American Society of Anesthesiologists grade III); (5) they were receiving anticoagulant medication; (6) they had severe comorbidity, including end-stage cardiovascular, pulmonary, liver or renal disease); (7) they were not able or refused to give informed written consent; (8) following enrolment and randomisation to one of the arms, those subjects who had inadequate colon preparation or in whom the caecum could not be reached were also excluded.
About this study
Official title: Computer-aided Detection With Deep Learning for Colorectal Adenoma During Colonoscopic Examination
We developed an artificial intelligent computer system with a deep neural network to analyze real-time video signals from the endoscopy station. This randomised controlled trial compared adenoma detection rate between computer-assisted colonoscopy and standard colonoscopy.
Read the detailed description
Colonoscopy is a primary screening and follow-up tool to detect colorectal cancer, a third leading cause of cancer death in Taiwan. Most colorectal cancers (CRCs) arise from preexisting adenomas, and the adenoma-carcinoma sequence offers an opportunity for the screening and prevention of CRCs. The removal of adenomatous polyps can lower the incidence of CRCs and result in reduced motality from CRCs. The adenoma detection rate, the proportion of screening colonoscopies performed by a endoscopist that detect at least one colorectal adenoma or adenocarcinoma, has been recommended as a quality indicator. The adenoma detection rate was inversely associated with the risks of interval colorectal cancer, advanced-stage interval cancer, and fatal interval cancer. However, adenoma detection rates vary widely among endoscopists in both academic and community settings. Polyp miss rates as high as 20% have been reported for high definition resolution colonoscopy. An improvement in adenoma detection rate at screening colonoscopy, translates into reduced risks of interval colorectal cancer and colorectal cancer death. Computer-aided detection of polyps might assist endoscopists to reduce the miss rate and enhance screening performance during colonoscopy. Computer-aided diagnosis and computer-aided detection are computerized systems that learn and inference in medical fields. Computer-aided diagnosis has been developed in colon polyp classification.
Computer-assisted image analysis has the potential to further aid adenoma detection but has remained underdeveloped. A notable benefit of such a system is that no alteration of the colonoscope or procedure is necessary. Machine learning with a deep neural network has been successfully applied to many areas of science and technology, such as object recognition and detection of computer vision, speech recognition, natural language processing. We developed an artificial intelligent computer system (PX-1) with a deep neural network to analyze real-time video signals from the endoscopy station. This randomised controlled trial compared ADR between computer-assisted colonoscopy and standard colonoscopy.
- Being tested
- Computer-aided detection, Standard colonoscopy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Computer-aided detectionDevice
We developed an artificial intelligent computer system with a deep neural network (PX-1) to analyze real-time video signals from the endoscopy station
Standard colonoscopyDevice
Standard colonoscopy
| Study group | Role | What they receive |
|---|---|---|
| Computer-aided detection | Experimental | Not described Receives: Device: Computer-aided detection |
| Standard colonoscopy | Placebo comparator | Not described Receives: Device: Standard colonoscopy |
What is being measured
The primary outcome is the main question the study is designed to answer.
Adenoma detection rate
Adenoma detection rate
Measured: During colonoscopic examination procedure
Secondary outcomes (1)
adenomas detected per subject
adenomas detected per subject
Measured: During colonoscopic examination procedure
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Feb 2019
- Study start1 Mar 2019 (estimated)
- Primary completion31 Dec 2021 (estimated)
- Study completion31 Dec 2021 (estimated)
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 Compare Between Computer-assisted Colonoscopy and Standard 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
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Care provider
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Tri-Service General Hospital (TSGH)
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Peng-Jen Chen, Tri-Service General Hospital (TSGH)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03842059
- Sponsor's protocol ID
- 107-2314-B-016 -011-MY2
- Status last verified
- Feb 2019
- First submitted
- 13 Feb 2019
- Last updated
- 15 Feb 2019
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Hsu WF, Kuo CY, Yen HH, Lin YM, Chen YN, Sun CK, Chang WY, Lin HH, Wu HY, Chang LC, Chang CY, Wu MS, Chiu HM. Computer-Assisted Colonoscopy in High-Adenoma Detection Rate Settings in a High-Risk Population: A Randomized Clinical Trial. JAMA Netw Open. 2026 Apr 1;9(4):e264881. doi: 10.1001/jamanetworkopen.2026.4881.
PubMed 41984482
Study at a glance
From the registry record, last updated by the sponsor on 15 Feb 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Computer-aided Detection With Deep Learning for Colorectal Adenoma During Colonoscopic Examination
- ClinicalTrials.gov ID
- NCT03842059
- Acronym / sponsor ID
- 107-2314-B-016 -011-MY2
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Tri-Service General Hospital (TSGH)University, hospital or other
- Study start
- 1 Mar 2019 (estimated)
- Primary completion
- 31 Dec 2021 (estimated)
- Study completion
- 31 Dec 2021 (estimated)
- First posted
- 15 Feb 2019 (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.
We developed an artificial intelligent computer system with a deep neural network to analyze real-time video signals from the endoscopy station. This randomised controlled trial compared adenoma detection rate between computer-assisted colonoscopy and standard colonoscopy.
Detailed description
Colonoscopy is a primary screening and follow-up tool to detect colorectal cancer, a third leading cause of cancer death in Taiwan. Most colorectal cancers (CRCs) arise from preexisting adenomas, and the adenoma-carcinoma sequence offers an opportunity for the screening and prevention of CRCs. The removal of adenomatous polyps can lower the incidence of CRCs and result in reduced motality from CRCs. The adenoma detection rate, the proportion of screening colonoscopies performed by a endoscopist that detect at least one colorectal adenoma or adenocarcinoma, has been recommended as a quality indicator. The adenoma detection rate was inversely associated with the risks of interval colorectal cancer, advanced-stage interval cancer, and fatal interval cancer. However, adenoma detection rates vary widely among endoscopists in both academic and community settings. Polyp miss rates as high as 20% have been reported for high definition resolution colonoscopy. An improvement in adenoma detection rate at screening colonoscopy, translates into reduced risks of interval colorectal cancer and colorectal cancer death. Computer-aided detection of polyps might assist endoscopists to reduce the miss rate and enhance screening performance during colonoscopy. Computer-aided diagnosis and computer-aided detection are computerized systems that learn and inference in medical fields. Computer-aided diagnosis has been developed in colon polyp classification.
Computer-assisted image analysis has the potential to further aid adenoma detection but has remained underdeveloped. A notable benefit of such a system is that no alteration of the colonoscope or procedure is necessary. Machine learning with a deep neural network has been successfully applied to many areas of science and technology, such as object recognition and detection of computer vision, speech recognition, natural language processing. We developed an artificial intelligent computer system (PX-1) with a deep neural network to analyze real-time video signals from the endoscopy station. This randomised controlled trial compared ADR between computer-assisted colonoscopy and standard colonoscopy.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, care provider)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Computer-aided detection | Experimental | Device: Computer-aided detection |
| Standard colonoscopy | Placebo comparator | Device: Standard colonoscopy |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Patients aged ≥20 years, scheduled for colonoscopy for one of the following indications for colonoscopy, were invited to participate in this study: polyp surveillance, changed bowel habits and/or bloody stools, bowel complaints, a positive family history for CRC, a positive FOBT, abdominal pain, diarrhoea, post-polypectomy surveillance.
Key exclusion criteria (1 listed)
- We excluded patients from this study if: (1) they had known colonic neoplasia or inflammatory or other significant colonic disease, such as patients specifically presenting for polypectomy; (2) there was open bleeding or they were receiving an emergency colonoscopy; (3) they had previously previous colonic resection; (4) they were in poor general condition (more than American Society of Anesthesiologists grade III); (5) they were receiving anticoagulant medication; (6) they had severe comorbidity, including end-stage cardiovascular, pulmonary, liver or renal disease); (7) they were not able or refused to give informed written consent; (8) following enrolment and randomisation to one of the arms, those subjects who had inadequate colon preparation or in whom the caecum could not be reached were also 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 |
|---|---|---|
| Computer-aided detectionWe developed an artificial intelligent computer system with a deep neural network (PX-1) to analyze real-time video signals from the endoscopy station | Device | Computer-aided detection |
| Standard colonoscopyStandard colonoscopy | Device | Standard colonoscopy |
Primary outcome
Adenoma detection rate
Time frame: During colonoscopic examination procedure
Adenoma detection rate
1 secondary outcome
adenomas detected per subject
Time frame: During colonoscopic examination procedure
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Feb 2019.
- Derived from this study: PubMed 41984482Hsu WF, Kuo CY, Yen HH, Lin YM, Chen YN, Sun CK, Chang WY, Lin HH, Wu HY, Chang LC, Chang CY, Wu MS, Chiu HM. Computer-Assisted Colonoscopy in High-Adenoma Detection Rate Settings in a High-Risk Population: A Randomized Clinical Trial. JAMA Netw Open. 2026 Apr 1;9(4):e264881. doi: 10.1001/jamanetworkopen.2026.4881.
- Individual participant dataSharing planned: Undecided
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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing Computer-aided detection and Standard colonoscopy in people with Compare Between Computer-assisted Colonoscopy and Standard Colonoscopy, run by Tri-Service General Hospital (TSGH).
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 1 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 15 Feb 2019. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. Some participants in this study may receive a placebo. 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?
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 detection rate (time frame: During colonoscopic examination 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 NCT03842059, 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 15 Feb 2019.