PDF herunterladen
Open Access
CC BY 4.0 · Endoscopy
DOI: 10.1055/a-2721-6798
Original article

A Prospective Study Evaluating an Artificial Intelligence-Based System for Withdrawal Time Measurement

Autoren

  • Ioannis Kafetzis

    1   Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Wurzburg, Wurzburg, Germany (Ringgold ID: RIN27207)

  • Philipp Sodmann

    1   Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Wurzburg, Wurzburg, Germany (Ringgold ID: RIN27207)

  • Bianca-Elena Herghelegiu

    2   Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Wurzburg, Würzburg, Germany (Ringgold ID: RIN27207)

  • Michela Pauletti

    3   Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Wurzburg, Würzburg, Germany (Ringgold ID: RIN27207)

  • Markus Brand 

    4   Gastroenterologie, Universitätsklinikum Würzburg, Würzburg, Germany (Ringgold ID: RIN27207)

  • Katrin Schöttker

    2   Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Wurzburg, Würzburg, Germany (Ringgold ID: RIN27207)

  • Wolfram G. Zoller

    5   Department of Internal Medicine and Gastroenterology, Klinikum Stuttgart Katharinenhospital, Stuttgart, Germany (Ringgold ID: RIN9203)

  • Jörg Albert

    5   Department of Internal Medicine and Gastroenterology, Klinikum Stuttgart Katharinenhospital, Stuttgart, Germany (Ringgold ID: RIN9203)

  • Alexander Meining

    6   Gastroenterologie, University Hospital Wurzburg, Würzburg, Germany (Ringgold ID: RIN27207)

  • Alexander Hann 

    1   Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Wurzburg, Wurzburg, Germany (Ringgold ID: RIN27207)

Gefördert durch: Eva Mayr-Stihl Stiftung

Clinical Trial:

Registration number (trial ID): NCT06094270, Trial registry: ClinicalTrials.gov (http://www.clinicaltrials.gov/), Type of Study: Prospective, single center, superiority


Weitere Informationen
Auch verfügbar auf
 als PDF herunterladen

Introduction Withdrawal time (WT) has emerged as a critical quality measure in colonoscopy for colorectal cancer screening. Due to the high variability in calculating WT, recent works have explored the use of artificial intelligence (AI) to standardize this process, but prospective validation remains elusive. Methods This prospective, superiority trial compared the accuracy of AI-assisted WT calculation to that of physicians during routine colonoscopy from December 2023 to March 2024. The gold standard was obtained via manual, frame-by-frame annotation of the examination video recordings. The AI also automatically generated an image report, which was qualitatively assessed by four endoscopists. ClinicalTrials.gov NCT06094270 Results In total, 126 patients recruited from December 2023 to March 2024 were analyzed. The proposed AI demonstrated a significantly lower mean absolute error (MAE) (2.2 minutes) in estimating WT compared to physicians (4.2 minutes; p<0.001). This is attributed to examinations containing endoscopic interventions where the AI had significantly lower MAE compared to physicians (2.1 vs 5.2; p<0.001). The MAE was comparable in the absence of interventions (2.3 vs 2.3; p=0.519). High-quality image reports were generated by the AI, with 97% of assessments indicating satisfaction with the timeline representation and 81% expressing overall satisfaction. Conclusion Our study demonstrates the superiority of an AI system in calculating WT during colonoscopy compared to physicians, providing significant improvements, especially in examinations containing interventions. This work demonstrates the promise of AI in streamlining clinical workflows.



Publikationsverlauf

Eingereicht: 03. Juni 2025

Angenommen nach Revision: 02. Oktober 2025

Accepted Manuscript online:
13. Oktober 2025

© . The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany