Ignite Creation Date:
2024-10-26 @ 3:43 PM
Last Modification Date:
2024-10-26 @ 3:43 PM
Study NCT ID:
NCT06656312
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-10-22
Brief Title:
Prospective Study of EndoAim ASUS AI Solution for Colorectal Polyp Diagnosis
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Prospective Study of ASUS Endoscopy AI Solution- EndoAim Assisting Diagnosis of Colorectal Polyps
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The colorectal cancer mortality rate in Taiwan ranks third among all cancers so its crucial to prevent colorectal cancer through regular colonoscopy screenings and remove polyps with higher cancer risk but during colonoscopy doctors tend to miss about 22 to 28 of polyps and 20 to 24 of these missed polyps may turn into cancerous adenomasintroducing 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 ADR PDR and 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
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 ADR PDR and 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 900000 people die from colorectal cancer in the US 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 we aim to offer robust proof of AIs utility in assisting diagnosis and support its broader clinical application
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
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 we aim to offer robust proof of AIs utility in assisting diagnosis and support its broader clinical application
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None