Clinical trial NCT06094270: Artificial-intelligence-based Reporting Technology for Endoscopy Monitoring and Imaging System
Official title: Artificial-intelligence-based Reporting Technology for Endoscopy Monitoring and Imaging System
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Colonoscopy
Keywords: Withdrawal Time
Medical Report
Colonoscopy
Artificial Intelligence
Endoscopy
Lead sponsor: Wuerzburg University Hospital
Sponsor class: OTHER
First posted: 2023-10-23
Enrollment: 147
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult patients (\>18 years) scheduled for screening, surveillance, or diagnostic colonoscopy

Brief 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.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

* Adult patients (\>18 years)
* Scheduled for colonoscopy

Exclusion Criteria:

Patient / Examination level

* Inflammatory Bowel Disease
* Familial Polyposis Syndrome
* Patient after radiation/resection of colonic parts

Data level

* Endoscopic recordings started after beginning of withdrawal.
* Examination recordings stopped before the end of the examination.
* Examinations with corrupt video signal

Primary outcomes:
measure: Withdrawal time error comparison for colonoscopies using the proposed AI system versus physician estimation
timeFrame: Through study completion, an average of 5 months
Secondary outcomes:
measure: Image quality satisfaction
timeFrame: Through study completion, an average of 5 months
measure: Subgroup analysis for withdrawal time calculation error based on the presence or absence of resections in the examination.
timeFrame: Through study completion, an average of 5 months
measure: Number of examination where withdrawal time could not be determined
timeFrame: Through study completion, an average of 5 months
Other outcomes:
Locations:
city: Würzburg
country: Germany
facility: Universitätsklinikum Würzburg
state: Bayern
Study officials:
References: