Accuracy of Real Time Characterization in Artificial Intelligence-assisted Colonoscopy

February 12, 2024 updated by: Ismail Gögenur

Accuracy of Real Time Characterization in Artificial Intelligence-assisted Colonoscopy - A Prospective Quality Assurance Study

The goal of this substudy is to investigate the accuracy of a computer-aided polyp characterization (CADx) system. The main question[s] it aims to answer are:

• How high is the specificity of the AI system when characterizing colorectal polyps

Participants will receive a standard colonoscopy, assisted by the artificial intelligence (AI) assisted system GI Genius.

Researchers will compare the AI system´s characterization with the histopathology to see how accurate the system is.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Detailed Description

Colorectal cancer (CRC) is the third most common cancer, and the second most common cause of cancer-related death worldwide. CRC screening is used for detection and removal of precancerous lesions before they develop into cancer. Colonoscopy is regarded being superior to other screening tests, and is therefore used as the golden standard.

Screening colonoscopy is associated with a reduced risk of CRC-related death. Since it is not possible for an endoscopist to determine the histopathology of the polyp with certainty during a colonoscopy, detected pre-malignant lesions should be removed and sent for histological examination. Multiple studies have shown that there is a strong association between findings at the baseline screening colonoscopy and rate of serious lesions at the follow up colonoscopy. Risk factors for adenoma, advanced adenoma and cancer at follow-up colonoscopy are multiplicity, size, villousness, and high degree dysplasia of the adenomas at the baseline screening colonoscopy.

Within the last few years there have been published several randomized controlled trials (RCT) investigating the efficacy of real time computer-aided detection. Studies have shown that AI contributes to a significantly higher adenoma detection rate (ADR), compared colonoscopies without assistance of an AI system.There have been concerns about prolonged colonoscopy time, and increased workload if implementing the AI-system, since the increased detection of small polyps may lead to unnecessary polypectomy.

With the development of computer-aided polyp characterization (CADx) systems, it is possible to use AI for decision support and not only for detection. There is no evidence yet that the CADx system increases the sensitivity for small neoplastic polyps when used by non-expert endoscopists (accredited for standard colonoscopy), but it may improve the clinicians confidence, and increase the specificity for optical diagnosis (Barua et al).

Diminutive polyps (1-5 mm) in the rectosigmoid colon can be left in situ when diagnosed with high confidence with a sensitivity of at least 90% and a specificity of at least 80%. To implement the resect-and-discard strategy, a sensitivity of at least 80% is acceptable. This is recommended by the European Society of Gastrointestinal Endoscopy (ESGE) as a strategy to decrease the unnecessary removal of small polyps with a negligible risk of harbouring cancer. Although the resect-and-discard strategy is assessed to be a safe and cost-effective method, it is important to be cautious with lesions in the right colon due to their malignant potential.

Reliable CADx systems could enable a more targeted removal of neoplastic polyps, while diminutive non-neoplastic polyps could be left behind. The potential excessive workload due to the CADe system could therefore theoretically be avoided by adding the CADx system.

The results so far are promising, suggesting that AI-assisted colonoscopy is superior to conventional colonoscopy when it comes to polyp and adenoma detection. Continued improvement of CADx systems in differentiating the pathology of colorectal lesions is needed, as well as additional clinical studies to assess the potential value of the CADx system.

The overall aim of this research is to investigate the quality, and the possible benefits of AI-assistance in colonoscopy. Hopefully this can contribute to a more accurate, safe, and targeted diagnosis and treatment of patients in the future.

The investigators have designed a quality assurance study to investigate the effect of real time AI-assisted colonoscopy with the CADx system (GI Genius, Medtronic). This study "REG-093-2022" is a substudy to the RCT "REG-092-2022". The investigators wish to evaluate the diagnostic accuracy of the CADx system.

Study Type

Interventional

Enrollment (Actual)

395

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Denmark
      • Holbæk, Denmark, 4300
        • Holbæk Hospital
      • Køge, Denmark, 4600
        • Zealand University Hospital
      • Nykøbing Falster, Denmark, 4800
        • Nykøbing Falster County Hospital
      • Næstved, Denmark, 4700
        • Næstved Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

  • Referred for screening colonoscopy due to a positive faecal immunochemical test (FIT) or for
  • Diagnostic colonoscopy due to symptoms/signs or
  • Post-polypectomy surveillance colonoscopy (only patients who had all detected polyps removed in the previous colonoscopy)

Exclusion Criteria:

  • Referral for removal of previous detected polyps
  • Emergency colonoscopy
  • Control colonoscopy due to inflammatory bowel disease (IBD)

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Other: AI-assisted colonoscopy
The patients in the intervention group will receive an AI-assisted colonoscopy (AIC) using the computer-aided polyp detection and characterization (CADe and CADx) GI Genius (Medtronic).
Device: AI-assisted colonoscopy
The patients will receive an AI-assisted colonoscopy (AIC) using the computer-aided polyp detection and characterization (CADe and CADx) GI Genius (Medtronic).

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
True positive findings: Adenomas (histopathologically verified) characterized as adenomas by the AI system
Time Frame: 5 Months
Data from the AI system will be compared with the histopathological data for each removed polyp
5 Months
True negative findings: Non-adenomas (histopathologically verified) characterized as non-adenomas by the AI system
Time Frame: 5 Months
Data from the AI system will be compared with the histopathological data for each removed polyp
5 Months
False positive findings: Non-adenomas (histopathologically verified) characterized as adenomas by the AI system
Time Frame: 5 Months
Data from the AI system will be compared with the histopathological data for each removed polyp
5 Months
False negative findings: Adenomas (histopathologically verified) characterized as non-adenomas by the AI system
Time Frame: 5 Months
Data from the AI system will be compared with the histopathological data for each removed polyp
5 Months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Ismail Gögenur

Collaborators

Holbaek Sygehus
Naestved Hospital
Nykøbing Falster County Hospital
Slagelse Hospital

Investigators

  • Principal Investigator: Ronja Lagström, MD, Zealand University Hospital, Køge

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 1, 2022

Primary Completion (Actual)

March 3, 2023

Study Completion (Estimated)

September 30, 2025

Study Registration Dates

First Submitted

February 3, 2023

First Submitted That Met QC Criteria

February 21, 2023

First Posted (Actual)

March 3, 2023

Study Record Updates

Last Update Posted (Actual)

February 13, 2024

Last Update Submitted That Met QC Criteria

February 12, 2024

Last Verified

February 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • REG-093-2022

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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