Adenoma Colon PolypNCT06656312
Prospective Study of EndoAim: ASUS AI Solution for Colorectal Polyp Diagnosis
A study testing Use ASUS EndoAim as an assistant software to perform colonoscopy in people with Adenoma Colon Polyp, run by Wen-Hsin Huang at 1 site in Taiwan.
Trial summary
"The colorectal cancer mortality rate in Taiwan ranks third among all cancers, so it is crucial to prevent colorectal cancer through regular colonoscopy screenings and remove polyps with higher cancer risk. Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Adenoma Colon Polyp
- What is being tested?
- Use ASUS EndoAim as an assistant software to perform colonoscopy
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 548 participants
- Where
- 1 siteTaiwan
- Study dates
- 28 Oct 2024to 14 Sep 2025
- 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
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Age 20 or older.
- Undergoing colonoscopy using the Olympus series endoscopes.
- Following a low-residue diet and undergoing bowel preparation.
Who cannot join
- Poor bowel cleansing. Note: Evaluated as Poor or Inadequate according to the Aronchick scale.
- Incomplete colonoscopy (not reaching the cecum).
- Use of anticoagulant medications or abnormal coagulation function within the last 5 days.
Trial locations and contacts
1 site in 1 country.
Taiwan1 site, 1 recruiting
- Recruiting
China Medical University Hospital
Taichung, North Dist., 404332
Hsing-Hung Cheng, MD, +886-975680861, 017124@tool.caaumed.org.tw
About this study
Official title: Prospective Study of ASUS Endoscopy AI Solution- EndoAim Assisting Diagnosis of Colorectal Polyps
"The colorectal cancer mortality rate in Taiwan ranks third among all cancers, so it is crucial to prevent colorectal cancer through regular colonoscopy screenings and remove polyps with higher cancer risk. However, during colonoscopy, doctors tend to miss about 22% to 28% of polyps, and 20% to 24% of these missed polyps may turn into cancerous adenomas. Introducing an Artificial Intelligence (AI) assisted system can improve the overall quality of colonoscopy.
This study aims to evaluate the effectiveness of the ASUS AI-assisted system (EndoAim) in diagnosing polyps during colonoscopy. It includes comparing the outcomes of colonoscopy with and without the use of EndoAim and assessing the impact of EndoAim on diagnostic effectiveness across different subgroups.
Each participant will be randomly assigned to undergo a colonoscopy with or without the assistance of EndoAim. The performance of the AI-assisted system in colonoscopy will be comprehensively evaluated using indicators such as APC(Adenoma Per Colonoscopy), ADR(Adenoma Detection Rate), PDR(Polyp Detection Rate), and Positive Predictive Value (PPV).. A subgroup analysis will also be conducted based on several important factors. Polyps will be biopsied and sent for pathological examination, with the pathology report serving as the final diagnosis for subsequent analysis."
Read the detailed description
"Background: According to the Health Promotion Administration of Taiwan and the American Cancer Society, colorectal cancer ranks 3rd in cancer-related deaths in Taiwan and 2nd in the United States. Each year, about 900,000 people die from colorectal cancer in the U.S. Before progressing to cancer, the removal of polyps can prevent colorectal cancer. Studies show that increasing the polyp detection rate by just 1% can reduce the risk of fatal colorectal cancer by 5%.
Colonoscopy is considered the gold standard for polyp removal. However, this procedure is technically demanding, time-consuming, and requires highly skilled physicians. Research indicates that 22% to 28% of polyps and 20% to 24% of precancerous adenomas are missed during colonoscopy. The main reasons include polyps being too small or flat, making them difficult to detect, or incomplete coverage of the colon during the procedure.
Recent advancements in Artificial Intelligence (AI) technology, especially in medical imaging, offer great potential in assisting diagnosis. AI-assisted systems can analyze images to help physicians detect and diagnose polyps more quickly and accurately during colonoscopies. This not only improves accuracy but also reduces the workload of physicians and increases the efficiency of the examination.
Implementing AI systems in colonoscopy can enhance the Adenoma Detection Rate (ADR) and Adenoma Per Colonoscopy (APC), while assisting in polyp characterization to help physicians determine treatment strategies. Thus, AI-supported colonoscopy procedures can improve both safety and effectiveness. While ADR has traditionally been the focus of most studies, APC provides a more comprehensive view of whether all adenomas are successfully removed. Therefore, this study will focus on APC as the primary indicator.
Study Design Objective:
This study aims to evaluate the effectiveness of the AI-assisted system (EndoAim) in diagnosing colorectal polyps during colonoscopy. The specific goals include:
- Comparing the effectiveness of standard colonoscopy with AI-assisted colonoscopy using EndoAim.
- Assessing the diagnostic performance of EndoAim across different subgroups (screening vs. surveillance, bowel cleanliness, physician experience, and polyp location).
Significance:
Building on existing literature, this study seeks to provide further evidence of the practical application of AI in colonoscopy. Through rigorous clinical trial design and extensive data analysis, robust proof of AI's utility in assisting diagnosis and support for broader clinical application will be offered.
Endpoints:
The primary endpoint is Adenoma Per Colonoscopy (APC). Secondary endpoints include Adenoma Detection Rate (ADR), Polyp Detection Rate (PDR), and Positive Predictive Value (PPV). These metrics will provide a comprehensive assessment of the effectiveness of the AI-assisted system in colonoscopy."
- Conditions
- Adenoma Colon Polyp
- Being tested
- Use ASUS EndoAim as an assistant software to perform colonoscopy
- Keywords
- polyp detection, AI-assisted system, endoscopy, deep learning, colonoscopy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Use ASUS EndoAim as an assistant software to perform colonoscopyDevice
Use ASUS EndoAim as an assistant software to perform colonoscopy
| Study group | Role | What they receive |
|---|---|---|
| AI Group | Experimental | Use ASUS EndoAim as an assistant software to perform colonoscopy Receives: Device: Use ASUS EndoAim as an assistant software to perform colonoscopy |
| Normal Group | No intervention | Standard Colonoscopy |
What is being measured
The primary outcome is the main question the study is designed to answer.
APC(Adenoma per colonocopy)
Rate of Adenoma Per Colonoscopy (APC) Increase in AI(Artificial Intelligence)-Assisted Group Compared to Standard Group Total Adenoma Polyps Findings/Total Performed Colonoscopy
Measured: 30 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Oct 2024
- Study start28 Oct 2024
- Primary completion14 Sep 2025 (estimated)
- Study completion14 Sep 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Adenoma Colon PolypStudies 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
- Single blind
- Who is masked
- Participant
- Enrolment
- 548 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Wen-Hsin Huang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Wen-Hsin Huang, China Medical University Hospital
- Investigators
- Wen-Hsin Huang, MD, Principal investigator, China Medical University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06656312
- Sponsor's protocol ID
- CMUH113-REC2-146
- Status last verified
- Oct 2024
- First submitted
- 22 Oct 2024
- Last updated
- 31 Oct 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 31 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Study of ASUS Endoscopy AI Solution- EndoAim Assisting Diagnosis of Colorectal Polyps
- ClinicalTrials.gov ID
- NCT06656312
- Acronym / sponsor ID
- CMUH113-REC2-146
- Conditions
- Adenoma Colon Polyp
- Keywords
- polyp detection, AI-assisted system, endoscopy, deep learning, colonoscopy
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 548 participants (estimated)
- Lead sponsor
- Wen-Hsin HuangUniversity, hospital or other
- Study start
- 28 Oct 2024 (actual)
- Primary completion
- 14 Sep 2025 (estimated)
- Study completion
- 14 Sep 2025 (estimated)
- First posted
- 24 Oct 2024 (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.
"The colorectal cancer mortality rate in Taiwan ranks third among all cancers, so it is crucial to prevent colorectal cancer through regular colonoscopy screenings and remove polyps with higher cancer risk. However, during colonoscopy, doctors tend to miss about 22% to 28% of polyps, and 20% to 24% of these missed polyps may turn into cancerous adenomas. Introducing an Artificial Intelligence (AI) assisted system can improve the overall quality of colonoscopy.
This study aims to evaluate the effectiveness of the ASUS AI-assisted system (EndoAim) in diagnosing polyps during colonoscopy. It includes comparing the outcomes of colonoscopy with and without the use of EndoAim and assessing the impact of EndoAim on diagnostic effectiveness across different subgroups.
Each participant will be randomly assigned to undergo a colonoscopy with or without the assistance of EndoAim. The performance of the AI-assisted system in colonoscopy will be comprehensively evaluated using indicators such as APC(Adenoma Per Colonoscopy), ADR(Adenoma Detection Rate), PDR(Polyp Detection Rate), and Positive Predictive Value (PPV).. A subgroup analysis will also be conducted based on several important factors. Polyps will be biopsied and sent for pathological examination, with the pathology report serving as the final diagnosis for subsequent analysis."
Detailed description
"Background: According to the Health Promotion Administration of Taiwan and the American Cancer Society, colorectal cancer ranks 3rd in cancer-related deaths in Taiwan and 2nd in the United States. Each year, about 900,000 people die from colorectal cancer in the U.S. Before progressing to cancer, the removal of polyps can prevent colorectal cancer. Studies show that increasing the polyp detection rate by just 1% can reduce the risk of fatal colorectal cancer by 5%.
Colonoscopy is considered the gold standard for polyp removal. However, this procedure is technically demanding, time-consuming, and requires highly skilled physicians. Research indicates that 22% to 28% of polyps and 20% to 24% of precancerous adenomas are missed during colonoscopy. The main reasons include polyps being too small or flat, making them difficult to detect, or incomplete coverage of the colon during the procedure.
Recent advancements in Artificial Intelligence (AI) technology, especially in medical imaging, offer great potential in assisting diagnosis. AI-assisted systems can analyze images to help physicians detect and diagnose polyps more quickly and accurately during colonoscopies. This not only improves accuracy but also reduces the workload of physicians and increases the efficiency of the examination.
Implementing AI systems in colonoscopy can enhance the Adenoma Detection Rate (ADR) and Adenoma Per Colonoscopy (APC), while assisting in polyp characterization to help physicians determine treatment strategies. Thus, AI-supported colonoscopy procedures can improve both safety and effectiveness. While ADR has traditionally been the focus of most studies, APC provides a more comprehensive view of whether all adenomas are successfully removed. Therefore, this study will focus on APC as the primary indicator.
Study Design Objective:
This study aims to evaluate the effectiveness of the AI-assisted system (EndoAim) in diagnosing colorectal polyps during colonoscopy. The specific goals include:
- Comparing the effectiveness of standard colonoscopy with AI-assisted colonoscopy using EndoAim.
- Assessing the diagnostic performance of EndoAim across different subgroups (screening vs. surveillance, bowel cleanliness, physician experience, and polyp location).
Significance:
Building on existing literature, this study seeks to provide further evidence of the practical application of AI in colonoscopy. Through rigorous clinical trial design and extensive data analysis, robust proof of AI's utility in assisting diagnosis and support for broader clinical application will be offered.
Endpoints:
The primary endpoint is Adenoma Per Colonoscopy (APC). Secondary endpoints include Adenoma Detection Rate (ADR), Polyp Detection Rate (PDR), and Positive Predictive Value (PPV). These metrics will provide a comprehensive assessment of the effectiveness of the AI-assisted system in colonoscopy."
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI Group | Experimental | Device: Use ASUS EndoAim as an assistant software to perform colonoscopyUse ASUS EndoAim as an assistant software to perform colonoscopy |
| Normal Group | No intervention | –Standard Colonoscopy |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Age 20 or older.
- Undergoing colonoscopy using the Olympus series endoscopes.
- Following a low-residue diet and undergoing bowel preparation.
Key exclusion criteria (3 listed)
- Poor bowel cleansing. Note: Evaluated as Poor or Inadequate according to the Aronchick scale.
- Incomplete colonoscopy (not reaching the cecum).
- Use of anticoagulant medications or abnormal coagulation function within the last 5 days.
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 |
|---|---|---|
| Use ASUS EndoAim as an assistant software to perform colonoscopyUse ASUS EndoAim as an assistant software to perform colonoscopy | Device | AI Group |
Primary outcome
APC(Adenoma per colonocopy)
Time frame: 30 days
Rate of Adenoma Per Colonoscopy (APC) Increase in AI(Artificial Intelligence)-Assisted Group Compared to Standard Group Total Adenoma Polyps Findings/Total Performed Colonoscopy
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Oct 2024.
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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.
Wen-Hsin Huang, MD principal investigator
China Medical University Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Use ASUS EndoAim as an assistant software to perform colonoscopy in people with Adenoma Colon Polyp, run by Wen-Hsin Huang at 1 site in Taiwan.
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 3 inclusion and 3 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 31 Oct 2024. 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 Taiwan. 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 Hsing-Hung Cheng, MD (+886-975680861, 017124@tool.caaumed.org.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
APC(Adenoma per colonocopy) (time frame: 30 days). 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 31 Oct 2024.