Real Life AI in Polyp Detection

Colonic Polyp Clinical Trial

— RELIANT  

Official title:

Real Life AI in Polyp Detection

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04335318
• Other study ID #: AI01
• Status: Completed
• Phase: N/A
• Start date: May 1, 2020
• Est. completion date: October 1, 2020

Study information

• Verified date: April 2021
• Source: Wuerzburg University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 230
• Est. completion date: October 1, 2020
• Est. primary completion date: August 31, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Colonoscopies for Polyp detection

Exclusion Criteria:

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

Related Conditions & MeSH terms

• Colonic Polyp
• Colonic Polyps
• Polyps

Intervention

Device:
• AI-assisted colonoscopy
Colonoscopies performed with assistance of an AI tool that highlights the areas that are susceptible to be a polyp.

Locations

Country	Name	City	State
Germany	Universitätsklinikum Würzburg	Würzburg	Bayern

Sponsors (1)

Lead Sponsor	Collaborator
Wuerzburg University Hospital

Country where clinical trial is conducted

Germany, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Polyp detection rate comparison	Number of polyps detected divided by number of colonoscopies	45 minutes
Primary	Mean withdrawal time comparison	Mean withdrawal time comparison	45 minutes
Secondary	AI-Polyp bounding boxes - True Positive Evaluation	2 approaches: frame by frame analysis and temporal coherence analysis	45 minutes
Secondary	AI-Polyp bounding boxes - False Positive Quantitative Evaluation	3 approaches depending on window-time detection	45 minutes
Secondary	AI-Polyp bounding boxes - False Negative Evaluation	Number of by bounding box missed polyps	45 minutes
Secondary	Reaction Time Analysis	Comparison time of polyp detection in a human vs machine approach	45 minutes