Colon Cancer, Colon Rectal Cancer
Conditions
Keywords
colonoscopy, Artificial intelligence
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 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.
Interventions
DEVICEEndoStyle
The EndoStyle system is able to transform the style of the different video-processor images.
Sponsors
Wuerzburg University Hospital
Study design
Observational model
CASE_ONLY
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Physicians with experience in colonoscopy.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Perceptual Indistinguishability of AI-Transformed Endoscopic Images | 5 months | Comparison of the accuracy for each study group. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Time Taken for Image Identification | 5 months | Time taken to assess the different tasks according to each study group. |
| Influence of regularly used processor | 5 months | Influence of regularly used endscopy processor during clinical work for the perceptual indistinguishability of AI-transformed endoscopic images with defined processor classes |
| Influence of endoscopy experience | 5 months | Influence of experience in endoscopy measured in lifetime performed colonscopies on perceptual indistinguishability of AI-transformed endoscopic images |
Countries
Germany
Outcome results
None listed