NCT07108569

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 identify the ability of physicians to distinguish artificial from real polyp images.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
53

participants targeted

Target at P25-P50 for not_applicable

Timeline
Completed

Started Nov 2024

Shorter than P25 for not_applicable

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

August 8, 2024

Completed
3 months until next milestone

Study Start

First participant enrolled

November 6, 2024

Completed
3 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

February 1, 2025

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

February 1, 2025

Completed
6 months until next milestone

First Posted

Study publicly available on registry

August 7, 2025

Completed
Last Updated

August 7, 2025

Status Verified

August 1, 2025

Enrollment Period

3 months

First QC Date

August 8, 2024

Last Update Submit

August 6, 2025

Conditions

Keywords

Colonoscopy

Outcome Measures

Primary Outcomes (1)

  • Ability to detect artificial images as artificial

    The ability to recognise artificial images as being artificial, using an online questionnaire - binary question

    6 months

Secondary Outcomes (2)

  • Ability to detect real images as real

    6 months

  • Accuracy to correctly classify images

    6 months

Study Arms (1)

Image assessment

EXPERIMENTAL

Physicians judge whether the random image presented to them is real or artificial

Other: Lutetia

Interventions

LutetiaOTHER

Lutetia is an AI-based training plattform

Image assessment

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • Physicians with or without experience in colonoscopy

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

University hospital Würzburg b

Würzburg, Germany

Location

MeSH Terms

Conditions

Colonic Polyps

Condition Hierarchy (Ancestors)

Intestinal PolypsPolypsPathological Conditions, AnatomicalPathological Conditions, Signs and Symptoms

Study Officials

  • Alexander Hann, MD

    Wuerzburg University Hospital

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NA
Masking
NONE
Masking Details
Participant does not know if pesented image is real or artificial
Purpose
BASIC SCIENCE
Intervention Model
SINGLE GROUP
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

August 8, 2024

First Posted

August 7, 2025

Study Start

November 6, 2024

Primary Completion

February 1, 2025

Study Completion

February 1, 2025

Last Updated

August 7, 2025

Record last verified: 2025-08

Locations