Ignite Creation Date:
2025-12-24 @ 12:34 PM
Ignite Modification Date:
2025-12-27 @ 10:09 PM
Study NCT ID:
NCT05513261
Status:
COMPLETED
Last Update Posted:
2025-02-24
First Post:
2022-08-22
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Clinical Validation of PolyDeep: an Artificial Intelligence-based Computer-aided Polyp Detection (CADe) and Characterization (CADx) System. Polydeep Advance 3
Sponsor:
Fundacin Biomedica Galicia Sur
Organization:
Study Overview
Official Title:
PolyDeep Advance 3: Randomized Clinical Trial Comparing High Definition Colonoscopy With PolyDeep Assisted High Definition Colonoscopy.
Status:
COMPLETED
Status Verified Date:
2023-11
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:
None
Brief Summary:
This study is a clinical validation of PolyDeep, a computer-aided polyp detection (CADe) and characterization (CADx) system. PolyDeep Advance 3 is a multicentric randomized clinical trial comparing high definition colonoscopy with PolyDeep assisted high definition colonoscopy. The hypothesis of the study is that the PolyDeep assisted colonoscopy increases the Adenoma Detection Rate (ADR).
Detailed Description:
Colorectal cancer (CRC) is the most frequently cancer in western world. A fundamental tool for detection and prevention is the colonoscopy. The detection and endoscopic resection of colorectal polyps, the precursor lesion of CRC, can reduce CRC incidence and mortality. Adenoma Detection Rate is the most used endoscopic quality indicator. The improvement of this indicator is related to the reduction of postcolonoscopy CRC incidence and mortality.
Colorectal polyp diagnosis is based on endoscopic resection and histological analysis. An accurate optical diagnosis could avoid histological lesion of smaller lesions, reducing the costs associated with histological diagnosis. The NICE (NBI International Colorectal Endoscopic) Classification has proposed use high definition endoscopes that have Narrow Band Imaging (NBI). However, NICE must be used by endoscopists who are sufficiently prepared and who have overcome the learning curve. Therefore, optical histology diagnosis with high accuracy independently of the center and the endoscopist is necessary.
Computer Aid Diagnosis (CAD) systems based on Artificial Intelligence are experiencing exponential development in the field of medical image analysis. The development of the CAD system is based on the creation of large databases of endoscopic images and/or videos, on the training, development and validation of diagnostic algorithms in such databases and, finally, on prospective clinical validation in patients undergoing colonoscopy. The goal of CAD systems in colonoscopy is double. First, the CAD system aims to increase the detection of polyps (CADe) in general, and of adenomas and serrated lesions in particular. The second objective is to characterize (CADx) the histology of detected lesion.
PolyDeep CAD is a functional prototype. This CAD system is capable of detecting, locating and classifying colorectal polyps. In vivo validation data shows that PolyDeep has high diagnostic accuracy for polyp identification and that this accuracy can be accommodated. The aim of PolyDeep advance 3 is to compare Adenoma Detection Rate differences in a randomized clinical trial. The investigators will compare the Adenoma Detection Rate between high definition colonoscopy and PolyDeep assisted high definition colonoscopy, both in CRC screening and surveillance.
Colorectal polyp diagnosis is based on endoscopic resection and histological analysis. An accurate optical diagnosis could avoid histological lesion of smaller lesions, reducing the costs associated with histological diagnosis. The NICE (NBI International Colorectal Endoscopic) Classification has proposed use high definition endoscopes that have Narrow Band Imaging (NBI). However, NICE must be used by endoscopists who are sufficiently prepared and who have overcome the learning curve. Therefore, optical histology diagnosis with high accuracy independently of the center and the endoscopist is necessary.
Computer Aid Diagnosis (CAD) systems based on Artificial Intelligence are experiencing exponential development in the field of medical image analysis. The development of the CAD system is based on the creation of large databases of endoscopic images and/or videos, on the training, development and validation of diagnostic algorithms in such databases and, finally, on prospective clinical validation in patients undergoing colonoscopy. The goal of CAD systems in colonoscopy is double. First, the CAD system aims to increase the detection of polyps (CADe) in general, and of adenomas and serrated lesions in particular. The second objective is to characterize (CADx) the histology of detected lesion.
PolyDeep CAD is a functional prototype. This CAD system is capable of detecting, locating and classifying colorectal polyps. In vivo validation data shows that PolyDeep has high diagnostic accuracy for polyp identification and that this accuracy can be accommodated. The aim of PolyDeep advance 3 is to compare Adenoma Detection Rate differences in a randomized clinical trial. The investigators will compare the Adenoma Detection Rate between high definition colonoscopy and PolyDeep assisted high definition colonoscopy, both in CRC screening and surveillance.
Study Oversight
Has Oversight DMC:
True
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?:
False
Is an FDA AA801 Violation?: