Colon CancerEndoStyleNCT06553326
EndoStyle: Artificial Intelligence Image Transformation Tool for Colonoscopy
An observational study of Colon Cancer and Colon Rectal Cancer, run by Wuerzburg University Hospital at 1 site in Germany.
Trial summary
The study addresses the limitations of current AI systems in gastrointestinal endoscopy, which are tipically trained with data from a single type of endoscopy processor and have limited expert-annotated images. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Colon Cancer, Colon Rectal Cancer
- What is being tested?
- EndoStyle
- Type of treatment
- Medical device
- How many people
- 40 participants
- Where
- 1 siteGermany
- Study dates
- 15 Aug 2024to 22 Aug 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The participants to the survey will be physicians with experience in colonoscopy.
Who may be able to join
- Physicians with experience in colonoscopy.
Who cannot join
None listed.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: EndoStyle: Survey of Physicians on Endoscopic Image Style Transfer.
The study addresses the limitations of current AI systems in gastrointestinal endoscopy, which are tipically trained with data from a single type of endoscopy processor and have limited expert-annotated images. The investigators aim to develop and validate EndoStyle, an AI system that can generate images in the style of various processors from a single reference image. EndoStyle will be tested by showing endoscopists colonoscopy sequences with different image types to determine if they can distinguish AI-transformed images. Success would enhance AI training for diverse clinical setups.
Read the detailed description
The use of artificial intelligence (AI) in gastrointestinal endoscopy has become widespread. However, these systems are often only trained with data from a single type of endoscopy processor, which limits their applicability. In addition, the availability of images annotated by experts is limited, which affects data variability and thus the performance of AI systems.
The aim of this study is to develop a new artificial intelligence (AI) based system (EndoStyle) and validate its authenticity by means of a survey among physicians, which is able to generate multiple images in the style of different processor types (including Olympus, Pentax and Storz) from a single endoscopy reference image.
The investigators hypothesis is that the AI system is able to successfully change the image style of video processors, with the differences being imperceptible to the endoscopist's eye.
The methodology consists of showing to multiple endoscopists 28 colonoscopy sequences of 10 seconds duration each. In each one of them 3 images will be shown that can be all the possible combinations of images belonging to positive control, negative control, and Endostyle (intervention group). By performing a statistical comparison of the percentages of selected images for each group the investigators will be able to establish whether the participants are able to distinguish the images transformed by the AI.
If the results corroborate our hypothesis, our system could generate images that would allow a more customized training of AI systems for each clinical setup.
- Conditions
- Colon Cancer, Colon Rectal Cancer
- Being tested
- EndoStyle
- Keywords
- colonoscopy, Artificial intelligence
- Medical subject headings
- Colonic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
EndoStyleDevice
The EndoStyle system is able to transform the style of the different video-processor images.
| Study group | Role | What they receive |
|---|---|---|
| Positive control | The image shown to the participant belongs to the same colonoscopy shown in the 10 second video-sequence. | |
| Negative control | The image shown to the participant belongs to the same colonoscopy shown in the 10 second video-sequence. | |
| EndoStyle (intervention group) | The image shown to the participant does not belong to the 10 second colonoscopy video-sequence but has been transformed with AI to simulate the style of the video. Receives: Device: EndoStyle |
What is being measured
The primary outcome is the main question the study is designed to answer.
Perceptual Indistinguishability of AI-Transformed Endoscopic Images
Comparison of the accuracy for each study group.
Measured: 5 months
Secondary outcomes (3)
Time Taken for Image Identification
Time taken to assess the different tasks according to each study group.
Measured: 5 months
Influence of regularly used processor
Influence of regularly used endscopy processor during clinical work for the perceptual indistinguishability of AI-transformed endoscopic images with defined processor classes
Measured: 5 months
Influence of endoscopy experience
Influence of experience in endoscopy measured in lifetime performed colonscopies on perceptual indistinguishability of AI-transformed endoscopic images
Measured: 5 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Aug 2024
- Study start15 Aug 2024
- Primary completion22 Aug 2025
- Study completion22 Aug 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Colon CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 40 participants (actual)
Sponsor and investigators
- Lead sponsor
- Wuerzburg University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Alexander Hann, MD, Principal investigator, Wuerzburg University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06553326
- Sponsor's protocol ID
- AI05
- Acronym
- EndoStyle
- Status last verified
- Aug 2025
- First submitted
- 8 Aug 2024
- Last updated
- 2 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 2 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- EndoStyle: Survey of Physicians on Endoscopic Image Style Transfer.
- ClinicalTrials.gov ID
- NCT06553326
- Acronym / sponsor ID
- EndoStyle / AI05
- Conditions
- Colon Cancer, Colon Rectal Cancer
- Keywords
- colonoscopy, Artificial intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 40 participants (actual)
- Lead sponsor
- Wuerzburg University HospitalUniversity, hospital or other
- Study start
- 15 Aug 2024 (actual)
- Primary completion
- 22 Aug 2025 (actual)
- Study completion
- 22 Aug 2025 (actual)
- First posted
- 14 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The study addresses the limitations of current AI systems in gastrointestinal endoscopy, which are tipically trained with data from a single type of endoscopy processor and have limited expert-annotated images. The investigators aim to develop and validate EndoStyle, an AI system that can generate images in the style of various processors from a single reference image. EndoStyle will be tested by showing endoscopists colonoscopy sequences with different image types to determine if they can distinguish AI-transformed images. Success would enhance AI training for diverse clinical setups.
Detailed description
The use of artificial intelligence (AI) in gastrointestinal endoscopy has become widespread. However, these systems are often only trained with data from a single type of endoscopy processor, which limits their applicability. In addition, the availability of images annotated by experts is limited, which affects data variability and thus the performance of AI systems.
The aim of this study is to develop a new artificial intelligence (AI) based system (EndoStyle) and validate its authenticity by means of a survey among physicians, which is able to generate multiple images in the style of different processor types (including Olympus, Pentax and Storz) from a single endoscopy reference image.
The investigators hypothesis is that the AI system is able to successfully change the image style of video processors, with the differences being imperceptible to the endoscopist's eye.
The methodology consists of showing to multiple endoscopists 28 colonoscopy sequences of 10 seconds duration each. In each one of them 3 images will be shown that can be all the possible combinations of images belonging to positive control, negative control, and Endostyle (intervention group). By performing a statistical comparison of the percentages of selected images for each group the investigators will be able to establish whether the participants are able to distinguish the images transformed by the AI.
If the results corroborate our hypothesis, our system could generate images that would allow a more customized training of AI systems for each clinical setup.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Positive control | – | –The image shown to the participant belongs to the same colonoscopy shown in the 10 second video-sequence. |
| Negative control | – | –The image shown to the participant belongs to the same colonoscopy shown in the 10 second video-sequence. |
| EndoStyle (intervention group) | – | Device: EndoStyleThe image shown to the participant does not belong to the 10 second colonoscopy video-sequence but has been transformed with AI to simulate the style of the video. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The participants to the survey will be physicians with experience in colonoscopy.
Key inclusion criteria (1 listed)
- Physicians with experience in colonoscopy.
Key exclusion criteria (0 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| EndoStyleThe EndoStyle system is able to transform the style of the different video-processor images. | Device | EndoStyle (intervention group) |
Primary outcome
Perceptual Indistinguishability of AI-Transformed Endoscopic Images
Time frame: 5 months
Comparison of the accuracy for each study group.
3 secondary outcomes
Time Taken for Image Identification
Time frame: 5 months
Influence of regularly used processor
Time frame: 5 months
Influence of endoscopy experience
Time frame: 5 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Sep 2025.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Alexander Hann, MD principal investigator
Wuerzburg University Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colon Cancer and Colon Rectal Cancer, run by Wuerzburg University Hospital at 1 site in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 0 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 2 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Perceptual Indistinguishability of AI-Transformed Endoscopic Images (time frame: 5 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Sep 2025.