AI-Assisted Colonoscopy for Polyp Detection
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May be covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: Northwestern University
No Placebo Group
Approved in 2 jurisdictions
Trial Summary
What is the purpose of this trial?Based on prior studies, trainee and practicing gastroenterologists miss pre-cancerous polyps (adenomas and serrated polyps) during colonoscopy. The use of computer-aided detection (CADe) systems, a form of artificial intelligence (AI) has been shown to help identify colorectal lesions for practicing gastroenterologists. However, less is known how AI impacts polyp detection for trainees.
The investigators are conducting a tandem colonoscopy study wherein a portion of the colon is examined first by the trainee and then the attending physician. For each procedure, randomization will occur which will determine whether or not the trainee will utilize AI for their examination of the colon. At the end of the study, the investigators will determine whether AI helps trainees miss fewer polyps during colonoscopy. The investigators will also conduct interviews with trainees to understand how AI impacts colonoscopy training.
Do I need to stop my current medications for the trial?
The trial information does not specify whether you need to stop taking your current medications.
What data supports the effectiveness of the treatment AI-Assisted Colonoscopy for Polyp Detection?
Research shows that using AI-assisted systems during colonoscopy can improve the detection of adenomas (a type of polyp that can turn into cancer) by highlighting their location, which helps doctors find them more easily. This approach has been shown to increase the adenoma detection rate (ADR) and reduce the number of missed adenomas, making the procedure more effective.
12345Is AI-assisted colonoscopy safe for humans?
There is no specific safety data available for AI-assisted colonoscopy in the provided research articles.
678910How is AI-assisted colonoscopy different from other treatments for polyp detection?
AI-assisted colonoscopy uses computer-aided detection (CADe) systems to help doctors find polyps more effectively during the procedure by highlighting their locations in real-time, which can reduce the number of missed polyps compared to traditional colonoscopy methods.
123411Eligibility Criteria
This trial is for trainee gastroenterologists performing colonoscopies. It aims to see if using AI can help them spot pre-cancerous polyps more effectively. Participants must be in training for gastroenterology and involved in conducting colonoscopies.Exclusion Criteria
I am referred for a procedure to remove polyps or for a colonoscopy.
I have had surgery on the right side of my colon.
Participant Groups
The study tests whether a computer-aided detection (CADe) system, which is a type of AI, helps trainees find more polyps during a colonoscopy compared to not using the technology. Trainees will either use AI or not by random choice and results are compared.
2Treatment groups
Active Control
Group I: Colonoscopy with AIActive Control1 Intervention
Trainee using AI during colonoscopy inspection
Group II: Colonoscopy without AIActive Control1 Intervention
Trainee not using AI during colonoscopy inspection
Colonoscopy With Computer-Aided Detection is already approved in United States, European Union for the following indications:
🇺🇸 Approved in United States as Colonoscopy with CADe for:
- Colorectal cancer screening
- Detection of adenomas and serrated polyps
🇪🇺 Approved in European Union as AI-Assisted Colonoscopy for:
- Colorectal cancer screening
- Detection of adenomas and serrated polyps
Find A Clinic Near You
Research locations nearbySelect from list below to view details:
Northwestern Memorial HospitalChicago, IL
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Who is running the clinical trial?
Northwestern UniversityLead Sponsor
References
Pilot study of a new freely available computer-aided polyp detection system in clinical practice. [2022]Computer-aided polyp detection (CADe) systems for colonoscopy are already presented to increase adenoma detection rate (ADR) in randomized clinical trials. Those commercially available closed systems often do not allow for data collection and algorithm optimization, for example regarding the usage of different endoscopy processors. Here, we present the first clinical experiences of a, for research purposes publicly available, CADe system.
Effect of real-time computer-aided detection of colorectal adenoma in routine colonoscopy (COLO-GENIUS): a single-centre randomised controlled trial. [2023]Artificial intelligence systems have been developed to improve polyp detection. We aimed to evaluate the effect of real-time computer-aided detection (CADe) on the adenoma detection rate (ADR) in routine colonoscopy.
The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study. [2022]Computer-aided detection (CADe) of colon polyps has been demonstrated to improve colon polyp and adenoma detection during colonoscopy by indicating the location of a given polyp on a parallel monitor. The aim of this study was to investigate whether embedding the CADe system into the primary colonoscopy monitor may serve to increase polyp and adenoma detection, without increasing physician fatigue level.
Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial). [2022]Artificial intelligence-based computer-aided polyp detection (CADe) systems are intended to address the issue of missed polyps during colonoscopy. The effect of CADe during screening and surveillance colonoscopy has not previously been studied in a United States (U.S.) population.
Real-Time Computer-Aided Detection of Colorectal Neoplasia During Colonoscopy : A Systematic Review and Meta-analysis. [2023]Artificial intelligence computer-aided detection (CADe) of colorectal neoplasia during colonoscopy may increase adenoma detection rates (ADRs) and reduce adenoma miss rates, but it may increase overdiagnosis and overtreatment of nonneoplastic polyps.
Colon Capsule Endoscopy: Indications, Findings, and Complications - Data from a Prospective German Colon Capsule Registry Trial (DEKOR). [2021]Reliable and especially widely accepted preventive measures are crucial to further reduce the incidence of colorectal cancer (CRC). Colon capsule endoscopy (CCE) might increase the screening numbers among patients unable or unwilling to undergo conventional colonoscopy. This registry trial aimed to document and determine the CCE indications, findings, complications, and adverse events in outpatient practices and clinics throughout Germany.
Detecting adverse events using information technology. [2022]Although patient safety is a major problem, most health care organizations rely on spontaneous reporting, which detects only a small minority of adverse events. As a result, problems with safety have remained hidden. Chart review can detect adverse events in research settings, but it is too expensive for routine use. Information technology techniques can detect some adverse events in a timely and cost-effective way, in some cases early enough to prevent patient harm.
Development of a Computer-Assisted Adverse Drug Events Alarm and Assessment System for Hospital Inpatients in China. [2021]Computerized detection is a promising method for monitoring adverse drug events (ADEs); however, this method is currently in its infancy and is a new area of exploration in China. This study aimed to develop a computerized ADE alarm and assessment system to help pharmacists effectively detect, assess, and analyze possible ADEs in patients in China.
Adverse drug event detection in a community hospital utilising computerised medication and laboratory data. [2018]Computerised monitors can detect and, with clinical intervention, often prevent or ameliorate adverse drug events (ADEs). We evaluated whether a computer-based alerting system was useful in a community hospital setting.
The impact of minor adverse event tracking on subject safety: a web-based system. [2009]BASED ON THE ASSUMPTION THAT MINOR symptoms may presage serious events, we report four years' experience with a web-based adverse event (AE) tracking system (eAETS) designed to capture AEs of a minor nature that would not meet criteria for ethical review. The eAETS has supported 175 diverse clinical protocols, is user-friendly and navigationally intuitive, and restricts access based on protocol ownership. The user creates an initial risk profile for comparison to subsequent AEs to identify unanticipated patterns. Out of 2,440 AE reports, 1,053 did not match the risk profile. Corrective modification was recommended in 6 (13%) protocols. The eAETS provides a framework for weighing the impact of AEs on subject safety.
Artificial Intelligence-Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial. [2023]Artificial intelligence (AI)-assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from large-scale multicenter randomized controlled trials (RCT) in an asymptomatic population are lacking.