Neoplasms

All Conditions

Symptoms and General Pathology

Adenoma

Colonic Polyps

Polyps

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

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

Fondazione Poliambulanza Istituto Ospedaliero

Digestive System Diseases

Carcinoma

Rectal Neoplasms

Adenocarcinoma

Colonic Neoplasms

Colorectal Neoplasms

Intestinal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Intestinal Diseases

Rectal Diseases

Colonic Diseases

Herbal and Botanical

All Drugs and Chemicals

Cola

Patients undergoing colonoscopy with standard colonscopy

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

Sebastian Manuel Milluzzo, MD

Paola Cesaro, MD, PhD

Paola Cesaro, MD, Phd

Cristiano Spada, MD, PhD

Even if colonoscopy is considered the reference standard for the detection of colonic neoplasia, polyps are still missed. The risk of early post-colonoscopy cancer appeared to be independently predicted by a relatively low polyp/adenoma detection rate. 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. Artificial intelligence 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 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).

Even 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 a Screening Setting

Resa Diagnostica Aggiuntiva Dell'Intelligenza Artificiale Nella Colonscopia (GENIAL COLONOSCOPY), Per lo Screening Del Carcinoma Colorettale

Visual Analogue Scale (VAS) (0 minimum to 10 maximum) will be recorded to assess the pain before and after the colonoscopy and results will be compared between the groups.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
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
Assessing the Additional Neoplasia Yield of Computer-aided Colonoscopy in a Screening Setting	NCT04441580	Colonic Neoplas... Colonic Adenoca... Polyps of Colon Rectal Neoplasm... Rectal Adenocar...	CADe colonoscop...	50 Years - 69 Years	Fondazione Poliambulanza Istituto Ospedaliero
Assessing the Additional Neoplasia Yield of Computer-aided Colonoscopy in a Screening Setting	NCT04441580	Colonic Neoplas... Colonic Adenoca... Polyps of Colon Rectal Neoplasm... Rectal Adenocar...	CADe colonoscop...	50 Years - 69 Years	Fondazione Poliambulanza Istituto Ospedaliero

Spots Global Cancer Trial Database for cade colonoscopy using gi genius device