Ignite Creation Date:
2024-05-06 @ 1:37 PM
Last Modification Date:
2024-10-26 @ 1:17 PM
Study NCT ID:
NCT04079478
Status:
COMPLETED
Last Update Posted:
2020-02-12
First Post:
2019-09-03
Brief Title:
The AID Study Artificial Intelligence for Colorectal Adenoma Detection
Sponsor:
Istituto Clinico Humanitas
Organization:
Istituto Clinico Humanitas
Study Overview
Official Title:
The AID Study Artificial Intelligence for Colorectal Adenoma Detection
Status:
COMPLETED
Status Verified Date:
2020-02
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
AID
Brief Summary:
Colonoscopy is clinically used as the gold standard for detection of colon cancer CRC and removal of adenomatous polyps Despite the success of colonoscopy in reducing cancer-related deaths there exists a disappointing level of adenomas missed at colonoscopy Back-to-back colonoscopies have indicated significant miss rates of 27 for small adenomas 5 mm and 6 for adenomas of more than 10 mm in diameter Studies performing both CT colonography and colonoscopy estimate that the colonoscopy miss rate for polyps over 10 mm in size may be as high as 12 The clinical importance of missed lesions should be emphasized because these lesions may ultimately progress to CRC8
Limitations in human visual perception and other human biases such as fatigue distraction level of alertness during examination increases such recognition errors and way of mitigating them may be the key to improve polyp detection and further reduction in mortality from CRC In the past years a number of CAD systems for detection of polyps from endoscopy images have been described However the benefits of traditional CAD technologies in colonoscopy appear to be contradictory therefore they should be improved to be ultimately considered useful Recent advances in artificial intelligence AI deep learning DL and computer vision have shown potential to assist polyp detection during colonoscopy
Limitations in human visual perception and other human biases such as fatigue distraction level of alertness during examination increases such recognition errors and way of mitigating them may be the key to improve polyp detection and further reduction in mortality from CRC In the past years a number of CAD systems for detection of polyps from endoscopy images have been described However the benefits of traditional CAD technologies in colonoscopy appear to be contradictory therefore they should be improved to be ultimately considered useful Recent advances in artificial intelligence AI deep learning DL and computer vision have shown potential to assist polyp detection during colonoscopy
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None