Real Life AI in Polyp Detection (RELIANT)

Wuerzburg University Hospital

Status: Completed

Conditions: Colonic Polyp

Treatments: Device: AI-assisted colonoscopy

Study type: Interventional

Funder types: Other

Identifiers

NCT04335318

AI01

Details and patient eligibility

About

The objective of this study is to compare the polyp detection rate (PDR) of endoscopists unaware of a commercially available artificial intelligence (AI) device for polyp detection during colonoscopy and the PDR of endoscopists with the aid of such a device. Moreover, an extensive characterization of the performance of this device will be done.

Full description

Recently, there have been remarkable breakthroughs in the introduction of deep learning techniques, especially convolutional neural networks (CNNs), in assisting clinical diagnosis in different medical fields. One of these artificial intelligence (AI) devices to diagnose colon polyps during colonoscopy was launched in October 2019. Its intended use is to work as an adjunct to the endoscopist during a colonoscopy with the purpose of highlighting regions with visual characteristics consistent with different types of mucosal abnormalities.

It is essential to know whether deep learning algorithms can really help endoscopists during colonoscopies. Several studies have already addressed this issue with different approaches and results. However, one common drawback of these type of Machine vs Human retrospective studies is endoscopist bias. It is usually generated because of human natural competitive spirit against machine or human relaxation because of AI-reliance. This can have an effect in the overall results.

The investigators perfomed colonoscopies with the use of a commercially available AI system to detect colonic polyps and recorded them during clinical routine. Additionally from March 2019 - May 2019, 120 colonoscopy videos were performed and captured prospectively without the use of AI.

In this study, the investigators plan to retrospectively compare those two video sets regarding the polyp detection rate, withdrawal time and polyp identification characteristics of the AI system.

Enrollment: 230 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Colonoscopies for Polyp detection

Exclusion criteria

• Colonoscopies for Inflammatory Bowel Disease (IBD).
• Colonoscopies for work up of an active bleeding

Trial design

Primary purpose: Diagnostic

Allocation: Non-Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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230 participants in 2 patient groups

Colonoscopy with AI-assistance group

Experimental group

Description:

Colonoscopies were performed with AI-assistance.

Treatment:

Device: AI-assisted colonoscopy

Standard Colonoscopy group

No Intervention group

Description:

Standard clinical procedure