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Training Physicians to Differentiate the Paris Classification Using Artificial Colon Polyp Images

NCT06550908 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 70

Last updated 2025-08-12

No results posted yet for this study

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.

Conditions

  • Colonic Polyp
  • Colon Adenoma

Interventions

OTHER

Lutetia Training Plattform - real images

Training platform Lutetia offers training the Paris classification using real images of colon polyps.

OTHER

Lutetia Training Plattform - artifical images

Training platform Lutetia offers training the Paris classification using artificial images of colon polyps.

Sponsors & Collaborators

  • Wuerzburg University Hospital

    lead OTHER

Principal Investigators

  • Alexander Hann · Wuerzburg University Hospital

Study Design

Allocation
RANDOMIZED
Purpose
BASIC_SCIENCE
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-04-15
Primary Completion
2025-08-31
Completion
2025-08-31

Countries

  • Germany

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06550908 on ClinicalTrials.gov