To hear about similar clinical trials, please enter your email below
Trial Title:
EndoStyle: Artificial Intelligence Image Transformation Tool for Colonoscopy
NCT ID:
NCT06553326
Condition:
Colon Cancer
Colon Rectal Cancer
Conditions: Official terms:
Rectal Neoplasms
Colonic Neoplasms
Conditions: Keywords:
colonoscopy
Artificial intelligence
Study type:
Observational
Overall status:
Not yet recruiting
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Device
Intervention name:
EndoStyle
Description:
The EndoStyle system is able to transform the style of the different video-processor
images.
Arm group label:
EndoStyle (intervention group)
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
endoscopist's 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.
Criteria for eligibility:
Study pop:
The participants to the survey will be physicians with experience in colonoscopy.
Sampling method:
Non-Probability Sample
Criteria:
Inclusion Criteria:
- Physicians with experience in colonoscopy.
Gender:
All
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
Accepts Healthy Volunteers
Start date:
August 15, 2024
Completion date:
December 31, 2024
Lead sponsor:
Agency:
Wuerzburg University Hospital
Agency class:
Other
Source:
Wuerzburg University Hospital
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT06553326
