CC BY-NC-ND 4.0 · Endoscopy 2023; 55(09): 871-876
DOI: 10.1055/a-2077-7398
Innovations and brief communications

Artificial intelligence-based polyp size measurement in gastrointestinal endoscopy using the auxiliary waterjet as a reference

Boban Sudarevic
1   Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
2   Department of Internal Medicine and Gastroenterology, Katharinenhospital, Stuttgart, Germany
,
Philipp Sodmann
1   Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
,
Ioannis Kafetzis
1   Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
,
1   Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
,
Thomas J. Lux
1   Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
,
Zita Saßmannshausen
1   Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
,
Katja Herlod
1   Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
,
3   Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
,
1   Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
,
Katrin Schöttker
1   Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
,
Wolfram G. Zoller
2   Department of Internal Medicine and Gastroenterology, Katharinenhospital, Stuttgart, Germany
,
Alexander Meining
1   Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
,
1   Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
› Author Affiliations
Supported by: Eva Mayr-Stihl Stiftung
Supported by: Fischerwerke GmbH & Co. KG
Supported by: Forum Gesundheitsstandort Baden-Württemberg
Supported by: Dieter von Holtzbrinck Foundation GmbH
Supported by: Bavarian Center for Cancer Research http://dx.doi.org/10.13039/501100022804


Abstract

Background Measurement of colorectal polyp size during endoscopy is mainly performed visually. In this work, we propose a novel polyp size measurement system (Poseidon) based on artificial intelligence (AI) using the auxiliary waterjet as a measurement reference.

Methods Visual estimation, biopsy forceps-based estimation, and Poseidon were compared using a computed tomography colonography-based silicone model with 28 polyps of defined sizes. Four experienced gastroenterologists estimated polyp sizes visually and with biopsy forceps. Furthermore, the gastroenterologists recorded images of each polyp with the waterjet in proximity for the application of Poseidon. Additionally, Poseidon's measurements of 29 colorectal polyps during routine clinical practice were compared with visual estimates.

Results In the silicone model, visual estimation had the largest median percentage error of 25.1 % (95 %CI 19.1 %–30.4 %), followed by biopsy forceps-based estimation: median 20.0 % (95 %CI 14.4 %–25.6 %). Poseidon gave a significantly lower median percentage error of 7.4 % (95 %CI 5.0 %–9.4 %) compared with other methods. During routine colonoscopies, Poseidon presented a significantly lower median percentage error (7.7 %, 95 %CI 6.1 %–9.3 %) than visual estimation (22.1 %, 95 %CI 15.1 %–26.9 %).

Conclusion In this work, we present a novel AI-based method for measuring colorectal polyp size with significantly higher accuracy than other common sizing methods.

Fig. 1 s–5 s, Appendix 1 s–64s, Tables 1 s–3 s,



Publication History

Received: 03 January 2023

Accepted after revision: 19 April 2023

Accepted Manuscript online:
20 April 2023

Article published online:
12 July 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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Rüdigerstraße 14, 70469 Stuttgart, Germany

 
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