Viewing Study NCT06553326



Ignite Creation Date: 2024-10-26 @ 3:37 PM
Last Modification Date: 2024-10-26 @ 3:37 PM
Study NCT ID: NCT06553326
Status: NOT_YET_RECRUITING
Last Update Posted: None
First Post: 2024-08-08

Brief Title: EndoStyle Artificial Intelligence Image Transformation Tool for Colonoscopy
Sponsor: None
Organization: None

Study Overview

Official Title: EndoStyle Survey of Physicians on Endoscopic Image Style Transfer
Status: NOT_YET_RECRUITING
Status Verified Date: 2024-08
Last Known Status: None
Delayed Posting: No
If Stopped, Why?: Not Stopped
Has Expanded Access: No
If Expanded Access, NCT#: N/A
Has Expanded Access, NCT# Status: N/A
Acronym: EndoStyle
Brief Summary: The study addresses the limitations of current AI systems in gastrointestinal endoscopy which are tipically trained with data from a single type of endoscopy processor and have limited expert-annotated images The investigators aim to develop and validate EndoStyle an AI system that can generate images in the style of various processors from a single reference image EndoStyle will be tested by showing endoscopists colonoscopy sequences with different image types to determine if they can distinguish AI-transformed images Success would enhance AI training for diverse clinical setups
Detailed Description: The use of artificial intelligence AI in gastrointestinal endoscopy has become widespread However these systems are often only trained with data from a single type of endoscopy processor which limits their applicability In addition the availability of images annotated by experts is limited which affects data variability and thus the performance of AI systems

The aim of this study is to develop a new artificial intelligence AI based system EndoStyle and validate its authenticity by means of a survey among physicians which is able to generate multiple images in the style of different processor types including Olympus Pentax and Storz from a single endoscopy reference image

The investigators hypothesis is that the AI system is able to successfully change the image style of video processors with the differences being imperceptible to the endoscopists eye

The methodology consists of showing to multiple endoscopists 28 colonoscopy sequences of 10 seconds duration each In each one of them 3 images will be shown that can be all the possible combinations of images belonging to positive control negative control and Endostyle intervention group By performing a statistical comparison of the percentages of selected images for each group the investigators will be able to establish whether the participants are able to distinguish the images transformed by the AI

If the results corroborate our hypothesis our system could generate images that would allow a more customized training of AI systems for each clinical setup

Study Oversight

Has Oversight DMC: None
Is a FDA Regulated Drug?: None
Is a FDA Regulated Device?: None
Is an Unapproved Device?: None
Is a PPSD?: None
Is a US Export?: None
Is an FDA AA801 Violation?: None