AI-Assisted Colonoscopy for Colorectal Cancer Detection

Overseen By Alex Rodriguez, BS

Age: Any Age

Sex: Any

Trial Phase: Academic

Sponsor: University of Southern California

Disqualifiers: Refusal of consent, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 4 Jurisdictions This treatment is already approved in other countries

Compensation: Varies

Number of Visits: 1

Duration: 2 years

25 Participants Needed

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. ^{1 2 3 4 5}

Is 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. ^{1 2 3 6 7}

How 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. ^{1 2 3 5 6}

Research Team

James Buxbaum, MD

Principal Investigator

University of Southern California

Eligibility 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.

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

Treatment Details

Interventions

• AI use in Endoscopy Room
• Non-AI use Standard of Care endoscopy room

Trial Overview 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).

Participant Groups

Group I: Non-Artificial Intelligence Endoscopy Room Active Control 1 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 Room Active Control 1 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

Who Is Running the Clinical Trial?

University of Southern California

Lead Sponsor

Trials

956

Recruited

1,609,000+

Findings from Research

In a study comparing 213 AI-assisted colonoscopies (AIAC) to 213 conventional colonoscopies (CC), the AIAC group had a significantly higher adenoma detection rate (ADR) of 47.9% compared to 38.5% in the CC group, indicating improved efficacy in detecting precancerous polyps.

The AIAC group also had a longer withdrawal time (15 minutes) compared to the CC group (13 minutes), suggesting that the AI technology may encourage more thorough examinations, although the overall polyp detection rate was similar between both groups.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial intelligence improves adenoma detection rate during colonoscopy. Schauer, C., Chieng, M., Wang, M., et al.[2022]

In a large-volume medical center, the introduction of artificial intelligence-aided colonoscopy (AIAC) did not improve adenoma and polyp detection rates, with rates of 30.3% for ADR and 36.5% for PDR compared to 35.2% and 40.9% in the pre-AIAC period.

Despite the lower detection rates, the AIAC group experienced significantly shorter procedure times, suggesting that while AIAC may streamline the process, it did not enhance the effectiveness of polyp detection.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial Intelligence-Aided Colonoscopy Does Not Increase Adenoma Detection Rate in Routine Clinical Practice. Levy, I., Bruckmayer, L., Klang, E., et al.[2023]

In a multicenter randomized controlled trial involving 3059 asymptomatic participants, AI-assisted colonoscopy significantly improved the overall adenoma detection rate (ADR) to 39.9% compared to 32.4% with conventional colonoscopy, indicating enhanced efficacy in detecting polyps.

The AI-assisted method also increased the detection of advanced adenomas and showed better performance for both expert and non-expert endoscopists, while slightly increasing the median withdrawal time during the procedure.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial Intelligence-Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial. Xu, H., Tang, RSY., Lam, TYT., et al.[2023]

AI-assisted endoscopy can significantly improve the detection rates of lesions, helping both experienced and trainee endoscopists by identifying abnormalities that may be missed during procedures.

The effectiveness of AI in this context has been evaluated in three key areas: detecting polyps, characterizing them as adenomatous or non-adenomatous, and predicting the likelihood of invasive cancer in polypoid lesions, enhancing diagnostic accuracy without extending procedure times.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Inteligencia artificial en la colonoscopia de tamizaje y la disminución del error. Galvis-García, E., Vega-González, FJ., Emura, F., et al.[2023]

Artificial intelligence-assisted colonoscopy (AIAC) significantly reduces the adenoma missed detection rate (AMR), with a decrease from 28.8% in the control group to 13.2% in the AIAC group, indicating improved diagnostic accuracy.

AIAC also enhances the adenoma detection rate (ADR), increasing from 28.7% in the control group to 36.4% in the AIAC group, suggesting that this technology can help detect more adenomas during colonoscopy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Effects of ai-assisted colonoscopy on adenoma miss rate/adenoma detection rate: A protocol for systematic review and meta-analysis. Shao, L., Yan, X., Liu, C., et al.[2023]

The use of computer-aided detection (CAD) significantly improved the sensitivity of polyp detection in CT colonography by less-experienced radiologists, increasing average sensitivity from 81.0% to 90.8%.

While CAD increased the number of false-positive results from 0.70 to 0.96 per patient, the overall improvement in detection rates suggests that CAD can enhance diagnostic accuracy for polyp identification.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer-aided detection of colorectal polyps: can it improve sensitivity of less-experienced readers? Preliminary findings. Baker, ME., Bogoni, L., Obuchowski, NA., et al.[2016]

A systematic review of six randomized controlled trials involving 5058 individuals showed that AI-assisted colonoscopy (AIAC) significantly increases adenoma detection rates (ADRs) to 33.7% compared to 22.9% with conventional colonoscopy (CC), indicating a strong efficacy of AI in improving detection of potential cancerous growths.

AIAC also resulted in a higher polyp detection rate (PDR) of 45.6% versus 30.6% for CC, although it did lead to a slightly longer total withdrawal time during the procedure.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis. Ashat, M., Klair, JS., Singh, D., et al.[2021]

References

1. New Zealand pubmed.ncbi.nlm.nih.gov

Artificial intelligence improves adenoma detection rate during colonoscopy. [2022]

2. United States pubmed.ncbi.nlm.nih.gov

Artificial Intelligence-Aided Colonoscopy Does Not Increase Adenoma Detection Rate in Routine Clinical Practice. [2023]

3. United States pubmed.ncbi.nlm.nih.gov

Artificial Intelligence-Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial. [2023]

4. Mexico pubmed.ncbi.nlm.nih.gov

Inteligencia artificial en la colonoscopia de tamizaje y la disminución del error. [2023]

5. United States pubmed.ncbi.nlm.nih.gov

Effects of ai-assisted colonoscopy on adenoma miss rate/adenoma detection rate: A protocol for systematic review and meta-analysis. [2023]

6. United States pubmed.ncbi.nlm.nih.gov

Computer-aided detection of colorectal polyps: can it improve sensitivity of less-experienced readers? Preliminary findings. [2016]

7. Germany pubmed.ncbi.nlm.nih.gov

Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis. [2021]