Real Life AI in Polyp Detection

Not Applicable

Completed

• Conditions: Colonic Polyp
• Interventions: Device: AI-assisted colonoscopy

• Registration Number: NCT04335318
• Lead Sponsor: Wuerzburg University Hospital

Brief Summary

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.

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 Timeline

• Study First Submitted: 2020-04-02
• Study First Submitted QC: 2020-04-02
• Study First Posted: 2020-04-06
• Study Start: 2020-05-01
• Primary Completion: 2020-08-31
• Study Completion: 2020-10-01
• Status Verified: 2021-04
• Last Update Submitted: 2021-04-07
• Last Update Posted: 2021-04-08

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 230

Inclusion Criteria

• Colonoscopies for Polyp detection

Exclusion Criteria

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

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Colonoscopy with AI-assistance group	AI-assisted colonoscopy	Colonoscopies were performed with AI-assistance.

Primary Outcome Measures

Name	Time	Method
Mean withdrawal time comparison	45 minutes	Mean withdrawal time comparison
Polyp detection rate comparison	45 minutes	Number of polyps detected divided by number of colonoscopies

Secondary Outcome Measures

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

Trial Locations

Locations (1)

Universitätsklinikum Würzburg; 🇩🇪 Würzburg, Bayern, Germany