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Trial Title:
Prospective Study of EndoAim: ASUS AI Solution for Colorectal Polyp Diagnosis
NCT ID:
NCT06656312
Condition:
Adenoma Colon Polyp
Conditions: Official terms:
Adenoma
Polyps
Conditions: Keywords:
polyp detection
AI-assisted system
endoscopy
deep learning
colonoscopy
Study type:
Interventional
Study phase:
N/A
Overall status:
Recruiting
Study design:
Allocation:
Randomized
Intervention model:
Parallel Assignment
Primary purpose:
Screening
Masking:
Single (Participant)
Intervention:
Intervention type:
Device
Intervention name:
Use ASUS EndoAim as an assistant software to perform colonoscopy
Description:
Use ASUS EndoAim as an assistant software to perform colonoscopy
Arm group label:
AI Group
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. 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."
Criteria for eligibility:
Criteria:
Inclusion Criteria:
1. Age 20 or older.
2. Undergoing colonoscopy using the Olympus series endoscopes.
3. Following a low-residue diet and undergoing bowel preparation.
Exclusion Criteria:
1. Poor bowel cleansing. Note: Evaluated as Poor or Inadequate according to the
Aronchick scale.
2. Incomplete colonoscopy (not reaching the cecum).
3. Use of anticoagulant medications or abnormal coagulation function within the last 5
days.
Gender:
All
Minimum age:
20 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
China Medical University Hospital
Address:
City:
Taichung
Zip:
404332
Country:
Taiwan
Status:
Recruiting
Contact:
Last name:
Hsing-Hung Cheng, MD
Phone:
+886-975680861
Email:
017124@tool.caaumed.org.tw
Start date:
October 28, 2024
Completion date:
September 14, 2025
Lead sponsor:
Agency:
Wen-Hsin Huang
Agency class:
Other
Source:
China Medical University Hospital
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT06656312
