Neoplasms

All Conditions

Adenoma

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Colonoscopy with the help of CADe system (ENDO-Aid)

ENDO-Aid by Olympus

The goal of this clinical trial is to evaluate the diagnostic yield of CADe in a consecutive population undergoing colonoscopy. The main question it aims to answer is the Adenoma Detection Rate (ADR). Participants undergoing colonoscopy will be randomized in a 1:1 ratio to either receive Computer-Aided Detection (CADe) colonoscopy or a conventional colonoscopy (CC). Researchers will compare the CADe group and the CC-group to see if CAD-e can increase the ADR significantly.

Accuracy of Endo-aid in Consecutive Patients Referred for Colonoscopy

Accuracy of Endo-aid in Consecutive Patients Referred for Colonoscopy - ACCENDO-Colo Study

ADR defined as the proportion of patients with at least 1 adenoma detected.

PDR defined as the proportion of patients with at least 1 polyp detected.

PPC defined as total number of polyps resected divided by the total number of colonoscopies.

APC defined as total number of histologically confirmed adenomas resected divided by the total number of colonoscopies.

SSLDR percentage of patients who have 1 or more histologically confirmed sessile serrated lesion resected divided by the total number of colonoscopies.

AADR Adenomas are classified as advanced with a size \>= 10mm and/or a (tubulo)villous histology and/or high-grade dysplasia (HGD)

Fondazione Poliambulanza Istituto Ospedaliero

Symptoms and General Pathology

Colonic Polyps

Polyps

Intestinal Polyps

Pathological Conditions, Anatomical

Patients undergoing colonoscopy with artificial intelligence.

We wanted to compare the diagnostic yield obtained by using CADe colonoscopy to the yield obtained by the standard colonoscopy (SC) in a follow-up pateints in the screening population.

CADe colonoscopy using GI Genius device

White light

Daniele Salvi

Paola Cesaro

daniele salvi

Cristiano Spada

The goal of this clinical trial is to evaluate the diagnostic yield of CADe in a consecutive population undergoing colonoscopy. The main question it aims to answer is the Adenoma Detection Rate (ADR). Participants undergoing colonoscopy for follow-up in a screening setting will be randomized in a 1:1 ratio to either receive Computer-Aided Detection (CADe) colonoscopy or a conventional colonoscopy (CC). GI Genius is the AI software that will be used in the present trial and is intended to be used as an adjunct to colonic endoscopy procedures to help endoscopists to detect in real time mucosal lesions (such as polyps and adenomas, including those with flat (non-polypoid) morphology) during standard screening and surveillance endoscopic mucosal evaluations. It is not intended to replace histopathological sampling as a means of diagnosis.Researchers will compare the CADe group and the CC-group to see if CAD-e can increase the ADR significantly.

en if colonoscopy is considered the reference standard for the detection of colonic neoplasia, polyps are still missed. In large administrative cohort or case-control studies, the risk of early post-colonoscopy cancer appeared to be independently predicted by a relatively low polyp/adenoma detection rate. The adenoma detection rate among different endoscopists has been shown to be strictly related with the risk of post-colonoscopy interval cancer. When considering the very high prevalence of advanced neoplasia in the FIT-positive enriched population, the risk of post-colonoscopy interval cancer due to a suboptimal quality of colonoscopy may be substantial. Available evidence justifies therefore the implementation of efforts aimed at improving adenoma detection rate, based on retraining interventions and on the adoption of innovative technologies, designed to enhance the accuracy of the endoscopic examination.Nowadays, Artificial intelligence (AI) is gaining increased attention and investigation, since it seems to improve the quality of medical diagnosis and treatment. In the field of gastrointestinal endoscopy, two potential roles of AI in colonoscopy have been examined so far: automated polyp detection (CADe) and automated polyp histology characterization (CADx). CADe can minimize the probability of missing a polyp during colonoscopy, thereby improving the adenoma detection rate (ADR) and potentially decreasing the incidence of interval cancer. GI Genius is the AI software that will be used in the present trial. The software is developed by Medtronic Inc. (Dublin, Ireland) and is intended to be used as an adjunct to colonic endoscopy procedures to help endoscopists to detect in real time mucosal lesions (such as polyps and adenomas, including those with flat (non-polypoid) morphology) during standard screening and surveillance endoscopic mucosal evaluations. It is not intended to replace histopathological sampling as a means of diagnosis.

The objective of this study was to compare the diagnostic yield obtained by using CADe colonoscopy to the yield obtained by the standard colonoscopy (SC). As the risk of progression is higher for large than for small adenomas the specific contribution of the new technique in reducing the miss rate of large neoplasms represents an important outcome to be assessed in the study. In addition, given the suggested association of a higher miss-rate of serrated and flat lesions with an increased risk of early post-colonoscopy CRC, the benefit of the new technique in reducing the miss rate of these lesions will be assessed.

Assessing the Additional Neoplasia Yield of Computer-aided Colonoscopy in Follow-up Patients in a Screening Setting

The percentage of patients with adenomas with high-grade displasia in CADe colonoscopy group will be recorded and compared with the rate of patients with adenomas with high-grade displasia in standard colonoscopy group.

The percentage of patients with 3 or more adenomas (serrated adenomas will also be considered in the calculation) in CADe colonoscopy group will be compared with the rate of patients with 3 or more adenomas (including serrated adenomas) in standard colonoscopy group.

The percentage of adenomas, polyps (in general), flat adenoma and serrated polyps/adenoma detected will be recorded and compared between the groups.

The size of lesion detected will be measured in millimiters and compared between the groups.

The percentage of patients with neoplasia of proximal (right colonic segments) or distal (left colonic segments and rectum) site will be assessed and compared between the groups.

the time interval, expressed in years, to the next suggested follow-up colonoscopy will be assessed and compared between the groups.

The time to reach the cecum will be measured in minutes, recorded and compared between the groups.

The time of withdrawal (from cecum to anus) and of the overall colonoscopy (from anus to anus) will be measured in minutes, recorded and compared between the groups.

The above-mentioned outcomes will be calculated for each endoscopist at 3, 6, 9 and 12 months.

Proportion of patients diagnosed with polyps which were detected only by Artificial intelligence

Digestive System Diseases

Colorectal Neoplasms

Intestinal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Colonic Diseases

Intestinal Diseases

Rectal Diseases

ENDOMIND

Alexander Hann

Evaluation of an artificial intelligence system for polyp detection (CADe)

We aim to evaluate the artificial intelligence system ENDOMIND that supports endoscopist in detection of polyps during surveillance endoscopy for colorectal cancer. 1070 patients out of 6 gastroenterologic practice are randomized 1:1 for conventional surveillance colonoscopy vs. surveillance with AI support. Primary endpoint is Adenoma detection rate.

Surveillance Modified by Artificial Intelligence in Endoscopy (SMARTIE)

Multicenter Evaluation Study of the CADe System ENDOMIND in Surveillance Endoscopy for Colorectal Cancer in German Outpatient Cohort

proportion of individuals undergoing a complete screening colonoscopy who have one or more adenomas

Exmainations with minimum one polyp detected.

minimum 0, Maximum 9; should be higher than 5 for appropriate surveillance

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Accuracy of Endo-aid in Consecutive Patients Referred for Colonoscopy	NCT05862948	Adenoma Colon Colonic Adenoma	ENDO-Aid by Oly...	18 Years - 85 Years	Fondazione Poliambulanza Istituto Ospedaliero
Assessing the Additional Neoplasia Yield of Computer-aided Colonoscopy in Follow-up Patients in a Screening Setting	NCT06160466	Colonic Adenoma Adenoma Polyp of Colon	CADe colonoscop... White light	50 Years - 74 Years	Fondazione Poliambulanza Istituto Ospedaliero
Surveillance Modified by Artificial Intelligence in Endoscopy (SMARTIE)	NCT05006092	Surveillance En... Artificial Inte... Polyps of Colon CADe	ENDOMIND	18 Years -	Wuerzburg University Hospital
Surveillance Modified by Artificial Intelligence in Endoscopy (SMARTIE)	NCT05006092	Surveillance En... Artificial Inte... Polyps of Colon CADe	ENDOMIND	18 Years -	Wuerzburg University Hospital

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Spots Global Cancer Trial Database for cade

Every month we try and update this database with for cade cancer trials from around the world to help patients and their families find trials that might be right for them. We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.

Every month we try and update this database with for cade cancer trials from around the world to help patients and their families find trials that might be right for them.
We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.