AI-Assisted Colonoscopy for Colorectal Cancer Detection
Recruiting in Palo Alto (17 mi)
Age: Any Age
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: University of Southern California
Disqualifiers: Refusal of consent, others
No Placebo Group
Approved in 4 Jurisdictions
Trial Summary
What is the purpose of this trial?Adenoma detection rate (ADR) is a validated quality metric for colonoscopy with higher ADR correlated with improved colorectal cancer outcomes. Artificial intelligence (AI) can automatically detect polyps on the video monitor which may allow endoscopists in training to improve their ADR. Objective and Purpose of the study: Measure the effect of AI in a prospective, randomized manner to determine its impact on ADR of Gastroenterology trainees.
Do I need to stop my current medications for this trial?
The trial protocol does not specify whether participants need to stop taking their current medications.
What data supports the effectiveness of the AI-Assisted Colonoscopy treatment for colorectal cancer detection?
Research suggests that AI-Assisted Colonoscopy can improve the detection of polyps, which are growths that can lead to colorectal cancer, by identifying areas that might be missed during a standard colonoscopy. However, some studies show mixed results, with AI not always increasing detection rates in real-world settings.
12345Is AI-assisted colonoscopy safe for humans?
AI-assisted colonoscopy has been studied in various trials, and while it improves the detection of polyps and adenomas (growths that can lead to cancer), there is no specific mention of safety concerns in the available research. This suggests that it is generally considered safe for use in humans.
12367How is AI-assisted colonoscopy different from other treatments for colorectal cancer detection?
AI-assisted colonoscopy uses artificial intelligence to help doctors find and identify polyps (small growths) during a colonoscopy, which can improve detection rates compared to traditional methods. This approach is unique because it leverages advanced technology to potentially reduce missed diagnoses, although its effectiveness in routine practice is still being evaluated.
12356Eligibility Criteria
This trial is for Gastroenterology fellows at USC who perform endoscopies. They must agree to participate and give informed consent. Procedures in intensive care or operating rooms, or those done solely by faculty without the fellow as primary operator, are excluded.Inclusion Criteria
All Gastroenterology fellows at USC performing Endoscopies will be included in the study.
Exclusion Criteria
Procedures performed only by faculty, in which the fellow is not the primary operator, will not be used for study metrics.
Fellows who refuse informed consent will be excluded
My procedure will be in the endoscopy unit, not in intensive care or the operating room.
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
2-4 weeks
Educational Session
Fellows undergo an educational session on quality metrics and AI software usage
1 week
1 visit (in-person)
Treatment
Fellows perform colonoscopies with and without AI to measure adenoma detection rate
2 years
Follow-up
Participants are monitored for safety and effectiveness after treatment
4 weeks
Participant Groups
The study aims to see if using AI during colonoscopy helps trainees find more polyps compared to standard methods without AI. It's a randomized test where some will use AI assistance and others won't, measuring the adenoma detection rate (ADR).
2Treatment groups
Active Control
Group I: Non-Artificial Intelligence Endoscopy RoomActive Control1 Intervention
The fellows will be randomized on a daily basis to perform colonoscopies in a non-AI endoscopy room (standard of care).
Group II: Artificial Intelligence Endoscopy RoomActive Control1 Intervention
The fellows will be randomized on a daily basis to perform colonoscopies in a room with AI (intervention)
AI use in Endoscopy Room is already approved in European Union, United States, Japan, Canada for the following indications:
🇪🇺 Approved in European Union as AIAC for:
- Colorectal cancer screening
- Polyp detection
🇺🇸 Approved in United States as AIAC for:
- Colorectal cancer screening
- Polyp detection
🇯🇵 Approved in Japan as AIAC for:
- Colorectal cancer screening
- Polyp detection
🇨🇦 Approved in Canada as AIAC for:
- Colorectal cancer screening
- Polyp detection
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
LAC+USC Medical CenterLos Angeles, CA
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Who Is Running the Clinical Trial?
University of Southern CaliforniaLead Sponsor
References
Artificial intelligence improves adenoma detection rate during colonoscopy. [2022]Artificial intelligence-assisted colonoscopy (AIAC) has gained attention as a tool to assist with polyp detection during colonoscopy. Uncertainty remains as to the clinical benefit, given limited publications using different modules.
Artificial Intelligence-Aided Colonoscopy Does Not Increase Adenoma Detection Rate in Routine Clinical Practice. [2023]The performance of artificial intelligence-aided colonoscopy (AIAC) in a real-world setting has not been described. We compared adenoma and polyp detection rates (ADR/PDR) in a 6-month period before (pre-AIAC) and after introduction of AIAC (GI Genius, Medtronic) in all endoscopy suites in our large-volume center. The ADR and PDR in the AIAC group was lower compared with those in the pre-AIAC group (30.3% vs 35.2%, P
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.
Inteligencia artificial en la colonoscopia de tamizaje y la disminución del error. [2023]Artificial Intelligence (AI) has the potential to change many aspects of healthcare practice. Image discrimination and classification has many applications within medicine. Machine learning algorithms and complicated neural networks have been developed to train a computer to differentiate between normal and abnormal areas. Machine learning is a form of AI that allows the platform to improve without being programmed. Computer Assisted Diagnosis (CAD) is based on latency, which is the time between the captured image and when it is displayed on the screen. AI-assisted endoscopy can increase the detection rate by identifying missed lesions. An AI CAD system must be responsive, specific, with easy-to-use interfaces, and provide fast results without substantially prolonging procedures. AI has the potential to help both, trained and trainee endoscopists. Rather than being a substitute for high-quality technique, it should serve as a complement to good practice. AI has been evaluated in three clinical scenarios in colonic neoplasms: the detection of polyps, their characterization (adenomatous vs. non-adenomatous) and the prediction of invasive cancer within a polypoid lesion.
Effects of ai-assisted colonoscopy on adenoma miss rate/adenoma detection rate: A protocol for systematic review and meta-analysis. [2023]Colonoscopy can detect colorectal adenomas and reduce the incidence of colorectal cancer, but there are still many missing diagnoses. Artificial intelligence-assisted colonoscopy (AIAC) can effectively reduce the rate of missed diagnosis and improve the detection rate of adenoma, but its clinical application is still unclear. This systematic review and meta-analysis assessed the adenoma missed detection rate (AMR) and the adenoma detection rate (ADR) by artificial colonoscopy.
Computer-aided detection of colorectal polyps: can it improve sensitivity of less-experienced readers? Preliminary findings. [2016]To determine whether computer-aided detection (CAD) applied to computed tomographic (CT) colonography can help improve sensitivity of polyp detection by less-experienced radiologist readers, with colonoscopy or consensus used as the reference standard.
Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis. [2021]Background and study aims  With the advent of deep neural networks (DNN) learning, the field of artificial intelligence (AI) is rapidly evolving. Recent randomized controlled trials (RCT) have investigated the influence of integrating AI in colonoscopy and its impact on adenoma detection rates (ADRs) and polyp detection rates (PDRs). We performed a systematic review and meta-analysis to reliably assess if the impact is statistically significant enough to warrant the adoption of AI -assisted colonoscopy (AIAC) in clinical practice. Methods  We conducted a comprehensive search of multiple electronic databases and conference proceedings to identify RCTs that compared outcomes between AIAC and conventional colonoscopy (CC). The primary outcome was ADR. The secondary outcomes were PDR and total withdrawal time (WT). Results  Six RCTs (comparing AIAC vs CC) with 5058 individuals undergoing average-risk screening colonoscopy were included in the meta-analysis. ADR was significantly higher with AIAC compared to CC (33.7 % versus 22.9 %; odds ratio (OR) 1.76, 95 % confidence interval (CI) 1.55-2.00; I 2  = 28 %). Similarly, PDR was significantly higher with AIAC (45.6 % versus 30.6 %; OR 1.90, 95 %CI, 1.68-2.15, I 2  = 0 %). The overall WT was higher for AIAC compared to CC (mean difference [MD] 0.46 (0.00-0.92) minutes, I 2  = 94 %). Conclusions  There is an increase in adenoma and polyp detection with the utilization of AIAC.