Neoplasms

All Conditions

Symptoms and General Pathology

Adenoma

Colonic Polyps

Polyps

Adenomatous Polyps

Pathological Conditions, Anatomical

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Intestinal Polyps

In the GGC arm, participants will undergo colonoscopy as per standard care for the unit where they are having their procedure, except that at some point prior to commencing withdrawal of the colonoscope, a member of the endoscopy staff will turn on the GI Genius machine. This will remain operational from the time it is switched on until the end of the procedure.

In the SC arm, participants will undergo colonoscopy as per standard care for the unit where they are having their procedure.

Participants will undergo diagnostic colonoscopy, which will be identical to the normal standard of care at the unit where they are undergoing their procedure, except that GI Genius will be turned on during the procedure.

GI Genius-assisted diagnostic colonoscopy

Diagnostic colonoscopy will be performed as per the standard of care for the unit where the patient is having their procedure.

Diagnostic Colonoscopy

Colin J Rees, MBBS

COLO-DETECT is a clinical trial to evaluate whether an Artificial Intelligence device ("GI Genius", manufactured by Medtronic) can identify more polyps (pre-cancerous growths of the bowel lining) during colonoscopy (large bowel camera test) than during colonoscopy without it.

Colorectal cancer is common, affecting 1 in 15 men and 1 in 18 women in the UK in their lifetime. Many colorectal cancers develop from polyps via the adenoma-carcinoma sequence: there is a pre-cancerous stage (adenoma) during which it is possible to remove the polyp and therefore prevent it from progressing to colorectal cancer. The gold standard tool for doing this is colonoscopy. However, colonoscopy does not pick up all polyps, particularly flat polyps.

Missed polyps can result in colorectal cancer, so it is imperative to detect and remove as many polyps as possible. Many different interventions have been introduced to improve polyp detection, the most recent of which is artificial intelligence devices. GI Genius is an artificial intelligence device which integrates with existing colonoscopy equipment and analyses the video feed from the colonoscope camera in real time. Any areas that may represent an abnormality are then highlighted (without any lag) within a green box, alerting the colonoscopist to its presence. The potential abnormality can then be assessed more closely by the colonoscopist to decide whether it needs to be removed or not.

COLO-DETECT is a 2-arm, prospective, randomised controlled trial to assess whether GI Genius is able to detect more polyps (specifically, adenomas) during colonoscopy than standard colonoscopy without GI Genius. The primary outcome will be the mean number of adenomas per procedure (MAP) and the key secondary outcome will be the proportion of colonoscopies in which one or more adenomas is detected (Adenoma Detection Rate - ADR). These are both important quality markers for colonoscopy; the study will be powered to detect a clinically meaningful difference in ADR, which will by default detect a meaningful difference in MAP as the sample size required for ADR is larger.

In addition to measuring the effect of GI Genius on polyp detection, COLO-DETECT will provide a health economics analysis concerning the use of GI Genius, perform long-term passive follow-up to examine for future outcomes related to colorectal polyps and colorectal cancer, and perform additional nested studies (subject to ethical approval) that examine the effect upon users (for example through a visual scanning study) and their experience of using the GI Genius.

The patient and colonoscopist (care provider) cannot be blinded to the allocation. The investigator and statistician will remain blinded to the allocation until analysis has been conducted.

COLO-DETECT: Can an Artificial Intelligence Device Increase Detection of Polyps During Colonoscopy?

COLO-DETECT: A Randomised Controlled Trial of Lesion Detection at Colonoscopy Using the GI Genius Artificial Intelligence Platform

Equipment, staff, histology, unplanned admission, and other related costs will be calculated and used to determine cost-effectiveness of GGC versus SC.

MAP values over the duration of the study, for colonoscopists not participating in the study but performing colonoscopy at study sites, will assist with baseline comparisons. These data are reported by endoscopy units as part of the normal endoscopy quality assurance programme.

ADR values over the duration of the study, for colonoscopists not participating in the study but performing colonoscopy at study sites, will assist with baseline comparisons. These data are reported by endoscopy units as part of the normal endoscopy quality assurance programme.

The number of adenomas identified during each colonoscopy will be summed and divided by the total number of colonoscopies performed. MAP is usually expressed as a number to one decimal place (e.g. 1.2).

Whether or not at least one adenoma is detected at colonoscopy will be determined for each participant. The number of colonoscopies where one or more adenomas is identified will be divided by the total number of colonoscopies to give the ADR. ADR is usually expressed as a percentage.

The number of adenomas identified during each colonoscopy within the 'screening' participant population will be summed and divided by the total number of colonoscopies in that participant population. The MAP for the 'screening' participant population within each study arm will be compared

The number of adenomas identified during each colonoscopy within the 'symptomatic' participant population will be summed and divided by the total number of colonoscopies in that participant population to calculate MAP. The MAP for the 'symptomatic' participant population within each study arm will be compared

Whether or not at least one adenoma is detected at colonoscopy will be determined for each participant within the 'screening' participant population. The number of colonoscopies where one or more adenomas is identified will be divided by the total number of colonoscopies in that participant population to calculate ADR. The ADR for the 'screening' participant population within each study arm will be compared

Whether or not at least one adenoma is detected at colonoscopy will be determined for each participant within the 'symptomatic' participant population. The number of colonoscopies where one or more adenomas is identified will be divided by the total number of colonoscopies in that participant population to calculate ADR. The ADR for the 'symptomatic' participant population within each study arm will be compared

The total number of polyps detected during each colonoscopy will be summed, and divided by the total number of colonoscopies, to calculate MPP. MPP is usually expressed as a number to one decimal place.

The total number of polyps detected during colonoscopy for each participant within the 'screening' participant population. will be summed, and divided by the total number of colonoscopies in that participant population, to calculate MPP. MPP is usually expressed as a number to one decimal place.

The total number of polyps detected during colonoscopy for each participant within the 'symptomatic' participant population will be summed, and divided by the total number of colonoscopies in that participant population, to calculate MPP. MPP is usually expressed as a number to one decimal place.

Whether or not at least one polyp is detected at colonoscopy will be determined for each participant. The number of colonoscopies where one or more polyps is detected will be divided by the total number of colonoscopies in that participant population to calculate PDR, which is normally expressed as a percentage.

Whether or not at least one polyp is detected at colonoscopy will be determined for each participant within the 'screening' participant population. The number of colonoscopies where one or more polyps is identified will be divided by the total number of colonoscopies in that participant population to calculate PDR, which is normally expressed as a percentage.

Whether or not at least one polyp is detected at colonoscopy will be determined for each participant within the 'symptomatic' participant population. The number of colonoscopies where one or more polyps is identified will be divided by the total number of colonoscopies in that participant population to calculate PDR, which is normally expressed as a percentage.

The location, size, and morphology of the polyps identified (and histology if retrieved) in each study arm will be compared. This will also be analysed for both the screening and symptomatic participant populations in each study arm.

The number of colonoscopies in each study arm in which one or more SSPs is identified, divided by the total number of colonoscopies in each arm. This will also be analysed for both the screening and symptomatic participant populations in each study arm.

The number of CRCs detected in each study arm divided by the total number of colonoscopies in each arm. This will include polyps removed and later found to cancerous on histology and lesions felt to be cancerous at the time of colonoscopy. This will also be analysed for both the screening and symptomatic participant populations in each study arm.

The number of AAs detected in each study arm divided by the total number of colonoscopies in each arm. This will also be analysed for both the screening and symptomatic participant populations in each study arm.

Caecal intubation rate (the proportion of colonoscopies in which the colonoscope reaches the furthest extent of the colon) will be compared between the study arms to assess for non-inferiority

Insertion time to caecum (time taken to reach the furthest point of the large bowel) will be compared between the study arms to assess for non-inferiority

Total time required to perform the colonoscopy will be compared between the study arms to assess for non-inferiority

Total withdrawal time (time taken to remove the colonoscope from the furthest point of the colon) in the absence of any polyps will be compared between the study arms to assess for non-inferiority

Colonoscopist-assessed patient comfort scores will be compared between the study arms to assess for non-inferiority

Nurse-assessed patient comfort scores will be compared between the study arms to assess for non-inferiority

A validated Patient-Reported Experience Measure (Newcastle ENDOPREM) will be used to compare patient experience of colonoscopy between study arms

The EuroQoL EQ-5D-5L (validated quality of life questionnaire) will be used to compare patient-reported health-related quality of life, between study arms

The need for further colonoscopy for each participant is determined by the findings at the index colonoscopy, according to national guidelines on polyp surveillance. This may differ between study arms if more polyps are identified in one arm.

Some colonoscopists partake in the national Bowel Cancer Screening Programme (BCSP) and some do not. MAP will be analysed by colonoscopist status within each study arm.

Some colonoscopists partake in the national Bowel Cancer Screening Programme (BCSP) and some do not. ADR will be analysed by colonoscopist status within each study arm.

MAP for the first 20 percent of participants will be compared to MAP for the last 20 percent of participants scoped by each participating colonoscopist, to assess for change over the course of the study.

ADR for the first 20 percent of participants will be compared to ADR for the last 20 percent of participants scoped by each participating colonoscopist, to assess for change over the course of the study.

MAP may vary from baseline, even in the control arm due to a contamination or learning effect; comparing baseline values to those during the study assesses for this effect.

ADR may vary from baseline, even in the control arm due to a contamination or learning effect; comparing baseline values to those during the study assesses for this effect.

North Wales Organisation for Randomised Trials in Health

Newcastle University

Medtronic

South Tyneside and Sunderland NHS Foundation Trust

Adenocarcinoma

Ultivision Artificial Intelligence enhanced screening colonoscopies will be performed.

Screening colonoscopies without Artificial Intelligence enhancement will be performed.

Ultivision Artificial Intelligence Software

No Artificial Intelligence Software enhancement

Efren Rael, MD

Andrew Ninh

Robert Shaffer, MD

Josie Summers

Cristina Ortega

Taddese T Desta, MD

Prateek Sharma, MD

April Higbee

Louis Y Korman, MD

Ira Breite, MD

Natalie Neri, RN

Cortney Vazquez

Keith Friedenberg, MD

Dana Zahid

Joon Lee

Bezawit Tekola, MD

David Johnson, MD

Nalini Guda, MD

The aim of this study is to assess the efficacy of Ultivision Artificial Intelligence (AI) Software in detecting adenomas in screening colonoscopy procedures. The safety of Ultivision AI Software will also be assessed. A subset of the subjects will enter a roll-in period for clinical trial safety assessment. The remainder of subjects who are eligible will enter the detection phase which comprises a screening colonoscopy procedure. In the detection phase, subjects will be randomized to a screening colonoscopy with Ultivision AI Software enhancement or without AI Software enhancement. The study will measure the mean adenomas per colonoscopy procedure, as defined by the protocol, detected while receiving either treatment option.

Triple (Participant, Pathologist, Outcomes Assessor)

To Compare Artificial Intelligence Software Aided Adenoma Detection in Screening Colonoscopies Versus Standard Colonoscopy Without Artificial Intelligence Software Assistance in Participants Between 45 and 75 Years of Age

AIDA: A Multicenter, Prospective, Randomized Controlled Trial to Evaluate the Clinical Efficacy and Safety of Artificial Intelligence (AI) Detection of Adenomas (AIDA) With Standard High Definition With Light in Screening Colonoscopy

Advanced adenoma detection rate (AADR) on SHDWLC using AI versus SHDWLC without AI.

Sessile serrated adenoma/polyp (SSA/P) detection rate on SHDWLC using AI versus SHDWLC without AI.

Polyp detection rate on SHDWLC using AI versus SHDWLC without AI.

Flat versus polypoid lesion detection rate on SHDWLC AI versus SHDWLC without AI.

Polyp location distribution on SHDWLC using AI versus SHDWLC without AI.

Surveillance-irrelevant lesion (SIL) and surveillance-relevant lesion (SRL) detection rates using SHDWLC AI versus SHDWLC without AI.

Recommended surveillance interval after using SHDWLC AI versus SHDWLC without AI.

Colonoscope withdrawal time using SHDWLC AI versus SHDWLC without AI.

The primary efficacy endpoint is the mean adenomas per colonoscopy procedure (MAP) detection, as defined by the protocol, with Ultivision AI software enhancement to screening standard high definition white light colonoscopy (SHDWLC) versus screening SHDWLC without AI.

The primary safety endpoint is the adenomas per extraction per colonoscopy procedure (APE), as defined by the protocol, using Ultivision AI software in subjects undergoing screening standard high definition white light colonoscopy (SHDWLC) versus SHDWLC without AI.

The secondary efficacy endpoint is the adenoma detection rate (ADR) defined as the ratio of screening colonoscopies with at least one adenoma, as defined by the protocol, detected divided by the total number of screening SHDWLC performed with Ultivision AI versus screening SHDWLC performed without AI.

Docbot, Inc.

Digestive System Diseases

Colorectal Neoplasms

Intestinal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Colonic Diseases

Intestinal Diseases

Rectal Diseases

After the patient has passed the screening, a routine colonoscopy will be performed, and the target tissue with suspected inflammation or neoplasia will be biopsied. The clinical investigators use the hyperspectral microscope to collect image information of the biopsy tissue in the endoscopy room. After collecting information, biopsy specimens will be routinely processed and sent for pathological diagnosis.

Xiuli Zuo, MD,PhD

The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of colorectal cancer other colorectal disease by marking and analyzing the characteristics of hyperspectral images based on the pathological results of colonoscopic biopsy, so as to improve the objectiveness and intelligence of early colorectal cancer diagnosis.

Prospectively collect the hyperspectral image information of ordinary colonoscopic biopsy tissue. The colonoscopic biopsy tissue is from the Endoscopy Center of Qilu Hospital of Shandong University. The hyperspectral images are marked based on the biopsy pathological results, and the deep convolutional neural network (DCNN) model is used. With training and verification, develop the Hyperspectral Imaging Artificial Intelligence Diagnostic System (HSIAIDS) .A portion of colonoscopic biopsy tissue will be collected as a prospective test set to prospectively test the diagnostic performance of the HSIAIDS algorithm.

Biopsies from the colonic or rectal polyps will be prospectively collected hyperspectral images and conducted for histology examination and model validation.

Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy

Accuracy of hyperspectral imaging (HSI) artificial intelligence model to identify colorectal hyperplastic polyp, adenoma, SSL and colorectal cancer. Accuracy of artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects \* 100%

Sensitivity of HSI artificial intelligence model Sensitivity = number of true positives / (number of true positives + number of false negatives) \* 100%.

Specificity of HSI Artificial Intelligence Model Specificity = number of true negatives / (number of true negatives + number of false positives))\*100%

Negative predictive values for HSI artificial intelligence model = number of true negatives / (number of true negatives + number of false negatives)\*100%

area under the receiver operating characteristic curve (AUC)

To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
COLO-DETECT: Can an Artificial Intelligence Device Increase Detection of Polyps During Colonoscopy?	NCT04723758	Colonic Polyp Colorectal Poly... Colorectal Aden... Colorectal Aden... Colorectal SSA Sessile Serrate... Sessile Colonic...	GI Genius-assis... Diagnostic Colo...	18 Years -	South Tyneside and Sunderland NHS Foundation Trust
To Compare Artificial Intelligence Software Aided Adenoma Detection in Screening Colonoscopies Versus Standard Colonoscopy Without Artificial Intelligence Software Assistance in Participants Between 45 and 75 Years of Age	NCT04196088	Colorectal Aden... Colorectal Poly... Colorectal Aden... Colorectal SSA	Ultivision Arti... No Artificial I...	45 Years - 75 Years	Docbot, Inc.
Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy	NCT05576506	Colorectal Neop... Colorectal Poly... Colorectal Aden... Colorectal SSA		18 Years - 75 Years	Shandong University
COLO-DETECT: Can an Artificial Intelligence Device Increase Detection of Polyps During Colonoscopy?	NCT04723758	Colonic Polyp Colorectal Poly... Colorectal Aden... Colorectal Aden... Colorectal SSA Sessile Serrate... Sessile Colonic...	GI Genius-assis... Diagnostic Colo...	18 Years -	South Tyneside and Sunderland NHS Foundation Trust
COLO-DETECT: Can an Artificial Intelligence Device Increase Detection of Polyps During Colonoscopy?	NCT04723758	Colonic Polyp Colorectal Poly... Colorectal Aden... Colorectal Aden... Colorectal SSA Sessile Serrate... Sessile Colonic...	GI Genius-assis... Diagnostic Colo...	18 Years -	South Tyneside and Sunderland NHS Foundation Trust
Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy	NCT05576506	Colorectal Neop... Colorectal Poly... Colorectal Aden... Colorectal SSA		18 Years - 75 Years	Shandong University

Spots Global Cancer Trial Database for colorectal ssa