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

NCT06550908 | Recruiting

• 1 other identifier: 2022120701-2
• Study Type: interventional
• Target: 70
• Locations: 1 country
• Sites: 1

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.

Conditions

Colonic Polyp; Colon Adenoma

Keywords

Colonoscopy

Outcome Measures

Primary Outcomes (1)

• Accuracy for Paris classification

  Ability to correctly classify colonic polyps using the Paris classification

  9 months

Secondary Outcomes (4)

• Range of misclassifications for Paris classification

  9 months

• Influence of endoscopy experience on accuracy for correct Paris classification

  9 months

• Influence of time to complete course on accuracy for correct Paris classification

  9 months

• Influence regular usage of Paris classification on accuracy for correct Paris classification

  9 months

Study Arms (2)

Training with real images

ACTIVE COMPARATOR

Physicians train using the Paris classification with real colon polyp images

Other: Lutetia Training Plattform - real images

Training with artificial images

EXPERIMENTAL

Physicians train using the Paris classification with artificial colon polyp images

Other: Lutetia Training Plattform - artifical images

Interventions

Lutetia Training Plattform - real images OTHER

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

Training with real images

Lutetia Training Plattform - artifical images OTHER

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

Training with artificial images

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Physicians with or without experience in colonoscopy

Sponsors & Collaborators

• Wuerzburg University Hospital: lead

Study Sites (1)

University hospital Würzburg

Würzburg, Germany

RECRUITING

MeSH Terms

Conditions

Colonic Polyps

Condition Hierarchy (Ancestors)

Intestinal Polyps; Polyps; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Study Officials

• Alexander Hann

  Wuerzburg University Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Alexander Hann, MD

CONTACT

0049931201; hann_a@ukw.de

Ronja Weber

CONTACT

0049931201; ronja.weber@stud-mail.uni-wuerzburg.de

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: BASIC SCIENCE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 8, 2024
• First Posted: August 13, 2024
• Study Start: April 15, 2025
• Primary Completion: August 31, 2025
• Study Completion: August 31, 2025
• Last Updated: August 12, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share

Locations

DE (1)