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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Image assessment | Experimental | Physicians judge whether the random image presented to them is real or artificial |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Lutetia | Other | Lutetia is an AI-based training plattform |
|
| Measure | Description | Time Frame |
|---|---|---|
| Ability to detect artificial images as artificial | The ability to recognise artificial images as being artificial, using an online questionnaire - binary question | 6 months |
| Measure | Description | Time Frame |
|---|---|---|
| Ability to detect real images as real | The ability to recognise real images as being real using an online questionnaire - binary question | 6 months |
| Accuracy to correctly classify images | Accuracy to correctly classify images using an online questionnaire |
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Inclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Alexander Hann, MD | Wuerzburg University Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University hospital Würzburg b | Würzburg | Germany |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 42344396 | Derived | Sodmann P, Weber R, Wettstein V, Kafetzis I, Chiang Y, Parayitam V, Pauletti M, Kouladouros K, Meining A, Hann A. Assessing realism of artificial intelligence-generated colorectal polyp images: International multicenter blinded reader study. Endosc Int Open. 2026 May 15;14:a28653480. doi: 10.1055/a-2865-3480. eCollection 2026. |
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| ID | Term |
|---|---|
| D003111 | Colonic Polyps |
| ID | Term |
|---|---|
| D007417 | Intestinal Polyps |
| D011127 | Polyps |
| D020763 | Pathological Conditions, Anatomical |
| D013568 | Pathological Conditions, Signs and Symptoms |
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Participant does not know if pesented image is real or artificial
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| 6 months |