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DOI: 10.1055/a-2721-6798
A prospective study evaluating an artificial intelligence-based system for withdrawal time measurement
Authors
Supported by: 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
Abstract
Background
Withdrawal time has emerged as a critical quality measure in colonoscopy for colorectal cancer screening. Owing to the high variability in calculating withdrawal time, recent research has explored the use of artificial intelligence (AI) to standardize this process, but prospective validation is lacking.
Methods
This prospective, superiority trial compared the accuracy of AI-assisted withdrawal time calculation with 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.
Results
126 patients were analyzed. The proposed AI system demonstrated a significantly lower mean absolute error (MAE) in estimating withdrawal time compared with physicians (2.2 vs. 4.2 minutes; P < 0.001). This was attributed to examinations containing endoscopic interventions, where the AI had significantly lower MAE compared with physicians (2.1 vs. 5.2; P < 0.001). The MAE was comparable in the absence of interventions (2.3 vs. 2.3; P = 0.52). High-quality image reports were generated by the AI system; 97% were assessed as showing satisfactory timeline representation and 81% achieved overall satisfaction.
Conclusion
Our study demonstrated the superiority of an AI system in calculating withdrawal time during colonoscopy compared with physicians, providing significant improvements, especially in examinations involving interventions. This work demonstrates the promise of AI in streamlining clinical workflows.
Publication History
Received: 03 June 2025
Accepted after revision: 02 October 2025
Accepted Manuscript online:
13 October 2025
Article published online:
20 November 2025
© 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
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