Colon Adenoma, Colonic Polyp
Conditions
Keywords
Colonoscopy
Brief summary
Training in endoscopy is essential for the early detection of precursors of colorectal cancer. Up to now, this training has been carried out with image collections of findings and in practice when working on patients. The investigators want to use artificial intelligence (AI) to better train doctors to recognise these precursors. By using generative AI, the investigators were able to create realistic images that comply with data protection regulations and whose content can be predefined. Parts of the image can also be regenerated so that it is possible to create different precancerous stages in the same place in the image. In this study the investigators want to train physicians using real images or artificial images in order to compare which version helps classify polyps better.
Interventions
OTHERLutetia Training Plattform - real images
Training platform Lutetia offers training the Paris classification using real images of colon polyps.
OTHERLutetia Training Plattform - artifical images
Training platform Lutetia offers training the Paris classification using artificial images of colon polyps.
Sponsors
Wuerzburg University Hospital
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
BASIC_SCIENCE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Physicians with or without experience in colonoscopy
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy for Paris classification | 9 months | Ability to correctly classify colonic polyps using the Paris classification |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Range of misclassifications for Paris classification | 9 months | Number of misclassification categorizes (eg 1-4) |
| Influence of endoscopy experience on accuracy for correct Paris classification | 9 months | Influence of endoscopy experience measured in number of perfomed colonoscopies on accuracy for correct Paris classification |
| Influence of time to complete course on accuracy for correct Paris classification | 9 months | Influence of time to complete course measured in days on accuracy for correct Paris classification |
| Influence regular usage of Paris classification on accuracy for correct Paris classification | 9 months | Influence regular usage of Paris classification (yes/no) on accuracy for correct Paris classification |
Countries
Germany
Contacts
Primary ContactAlexander Hann, MD
Backup ContactRonja Weber
Outcome results
None listed