Real Life AI in Polyp Detection (RELIANT)

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.

Study Overview

• Status: Completed
• Conditions: Colonic Polyp
• Intervention / Treatment: Device: AI-assisted colonoscopy

Detailed 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.

• Study Type: Interventional
• Enrollment (Actual): 230
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Germany
  • Bayern
    • Würzburg, Bayern, Germany, 97080
      • Universitatsklinikum Wurzburg

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• Colonoscopies for Polyp detection

Exclusion Criteria:

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: DIAGNOSTIC
• Allocation: NON_RANDOMIZED
• Interventional Model: PARALLEL
• Masking: NONE

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
EXPERIMENTAL: Colonoscopy with AI-assistance group; Colonoscopies were performed with AI-assistance.	Device: AI-assisted colonoscopy; Colonoscopies performed with assistance of an AI tool that highlights the areas that are susceptible to be a polyp.
NO_INTERVENTION: Standard Colonoscopy group; Standard clinical procedure

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Polyp detection rate comparison	Number of polyps detected divided by number of colonoscopies	45 minutes
Mean withdrawal time comparison	Mean withdrawal time comparison	45 minutes

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
AI-Polyp bounding boxes - True Positive Evaluation	2 approaches: frame by frame analysis and temporal coherence analysis	45 minutes
AI-Polyp bounding boxes - False Positive Quantitative Evaluation	3 approaches depending on window-time detection	45 minutes
AI-Polyp bounding boxes - False Negative Evaluation	Number of by bounding box missed polyps	45 minutes
Reaction Time Analysis	Comparison time of polyp detection in a human vs machine approach	45 minutes

Collaborators and Investigators

• Sponsor: Wuerzburg University Hospital
• Investigators: Principal Investigator: Alexander Hann, PD Dr. Med, Wuerzburg University Hospital

Study record dates

Study Major Dates

• Study Start (ACTUAL): May 1, 2020
• Primary Completion (ACTUAL): August 31, 2020
• Study Completion (ACTUAL): October 1, 2020

Study Registration Dates

• First Submitted: April 2, 2020
• First Submitted That Met QC Criteria: April 2, 2020
• First Posted (ACTUAL): April 6, 2020

Study Record Updates

• Last Update Posted (ACTUAL): April 8, 2021
• Last Update Submitted That Met QC Criteria: April 7, 2021
• Last Verified: April 1, 2021

More Information

Terms related to this study

• Keywords: Deep Learning; Polyp detection rate; CNN
• Additional Relevant MeSH Terms: Pathological Conditions, Anatomical; Intestinal Polyps; Polyps; Colonic Polyps
• Other Study ID Numbers: AI01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No