Clinical Trial Details
— Status: Recruiting
Administrative data
NCT number |
NCT06094270 |
Other study ID # |
AI03 |
Secondary ID |
|
Status |
Recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
December 19, 2023 |
Est. completion date |
September 30, 2024 |
Study information
Verified date |
May 2024 |
Source |
Wuerzburg University Hospital |
Contact |
Alexander Hann |
Phone |
+49 931 201-45918 |
Email |
hann_a[@]ukw.de |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
Properly documenting withdrawal time in colonoscopy is essential for quality assessment and
cost allocation. However, reporting withdrawal time has significant interobserver
variability. Additionally, current manual documentation of endoscopic findings is
time-consuming and distracting for the physician. This trial examines an artificial
intelligence based system to determine withdrawal time and create a structured report,
including high-quality images (AI) of detected polyps and landmarks.
Description:
This study aims to compare withdrawal time precision calculated by an AI system with
examiner-reported times during colonoscopy, also evaluating endoscopists' satisfaction with
the images included in the AI-generated reports. The study will be single-center and
endoscopist-blinded, where 138 patients are expected to be recruited, taking polyp detection
rates and potential dropouts into consideration. Manual annotation of withdrawal times from
examination recordings will establish gold standard annotations. The AI system performs a
frame-by-frame analysis of endoscopy recordings, predicting endoscopic findings. Using a
rule-based logic, the method calculates withdrawal time for the examination and automatically
generates a report for the examination. The study will include consenting adult patients
eligible for colonoscopy, excluding those meeting specific criteria.
In this observational study, the withdrawal time for the examinations of all recruited
patients is estimated by both the physician and the AI method. The study does not relate to
any particular indication, and any patient that is appointed for a colonoscopy and does not
meet the exclusion criteria can be recruited. The AI method operates in the background,
having no influence on the examination's process, or outcome. The standard procedure requires
physicians to estimate the withdrawal time and document it in the examination report.
Simultaneously, the proposed AI method also computes the withdrawal time for all patients in
the background, without affecting the physician, the examination, or the outcomes of the
examination. Importantly, the physician remains blinded to the AI model's output.
To establish the gold standard withdrawal time, manual calculations will be performed using
the recorded examination data for all patients. This gold standard is used for evaluating
errors in withdrawal time estimation made by both the physician and the AI method.
Subsequently, a comparative analysis is conducted to assess the disparities between the
physician's estimations and those of the AI method.
Furthermore, the AI method captures characteristic images of anatomical landmarks and notable
events, such as polyp resections, during the examination. A panel of certified endoscopists
will rigorously evaluate the quality and relevance of these selected images.