Artificial-intelligence-based Reporting Technology for Endoscopy Monitoring and Imaging System

NCT ID: NCT06094270

Last Updated: 2025-04-03

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 147 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-12-19
• Study Completion Date: 2024-03-27

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.

Conditions

Colonoscopy

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: PROSPECTIVE

Study Groups

Experimental: Intervention arm

All patients within the study are included in the intervention arm: The withdrawal time for the interventions for all patients is documented by the physician and the proposed AI system.

EndoMind

Intervention Type: DEVICE

Withdrawal time is calculated and an image report is generated using the EndoMind system.

Interventions

EndoMind

Withdrawal time is calculated and an image report is generated using the EndoMind system.

Intervention Type: DEVICE

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Wuerzburg University Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Universitätsklinikum Würzburg

Würzburg, Bavaria, Germany

Countries

Germany

References

Kafetzis I, Sodmann P, Herghelegiu BE, Pauletti M, Brand M, Schottker K, Zoller WG, Albert J, Meining A, Hann A. A Prospective Study Evaluating an Artificial Intelligence-Based System for Withdrawal Time Measurement. Endoscopy. 2025 Oct 13. doi: 10.1055/a-2721-6798. Online ahead of print.

Reference Type: DERIVED

PMID: 41082920 (View on PubMed)

Other Identifiers

AI03

Identifier Type: -

Identifier Source: org_study_id