Clinical trial NCT04335318: Real Life AI in Polyp Detection
Official title: Real Life AI in Polyp Detection
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Colonic Polyp
Keywords: Deep Learning
Polyp detection rate
CNN
Lead sponsor: Wuerzburg University Hospital
Sponsor class: OTHER
First posted: 2020-04-06
Enrollment: 230
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.

Eligibility criteria: Inclusion Criteria:

* Colonoscopies for Polyp detection

Exclusion Criteria:

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

Primary outcomes:
measure: Polyp detection rate comparison
timeFrame: 45 minutes
measure: Mean withdrawal time comparison
timeFrame: 45 minutes
Secondary outcomes:
measure: AI-Polyp bounding boxes - True Positive Evaluation
timeFrame: 45 minutes
measure: AI-Polyp bounding boxes - False Positive Quantitative Evaluation
timeFrame: 45 minutes
measure: AI-Polyp bounding boxes - False Negative Evaluation
timeFrame: 45 minutes
measure: Reaction Time Analysis
timeFrame: 45 minutes
Other outcomes:
Locations:
city: Würzburg
country: Germany
facility: Universitätsklinikum Würzburg
state: Bayern
Study officials:
affiliation: Wuerzburg University Hospital
name: Alexander Hann, PD Dr. Med
role: PRINCIPAL_INVESTIGATOR
References: