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CC BY 4.0 · Endoscopy
DOI: 10.1055/a-2642-7584
Original Article

Evaluation of an improved computer-aided detection system for Barrett’s neoplasia in real-world imaging conditions

Martijn R. Jong‡   ‡
1   Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
,
Rixta A. H. van Eijck van Heslinga‡   ‡
1   Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
,
Carolus H. J. Kusters
2   Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
,
Tim J. M. Jaspers
2   Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
,
Tim G. W. Boers
2   Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
,
Lucas C. Duits
1   Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
,
Roos E. Pouw
1   Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
,
Bas L. A. M. Weusten 
3   Department of Gastroenterology and Hepatology, St Antonius Hospital, Nieuwegein, The Netherlands
4   Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
,
Alaa Alkhalaf
5   Department of Gastroenterology and Hepatology, Isala Hospital, Zwolle, The Netherlands
,
Fons van der Sommen
2   Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
,
Peter H. N. de With
2   Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
,
Albert J. de Groof
1   Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
,
Jacques J. Bergman 
1   Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
,
on behalf of the BONSAI Consortium (All members and collaborators of the “Barrett’s Oesophagus Imaging for Artificial Intelligence” (BONS-AI) consortium are listed in the online-only Supplementary materials.)› Institutsangaben
Gefördert durch: KWF Kankerbestrijding http://dx.doi.org/10.13039/501100004622 Gefördert durch: DANAE project, supported by the NWO/KWF foundation Gefördert durch: Nederlandse Organisatie voor Wetenschappelijk Onderzoek Gefördert durch: DANAE project, supported by the NWO/KWF foundation http://dx.doi.org/10.13039/501100003246
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Abstract

Background Computer-aided detection (CADe) systems may improve detection of Barrett’s neoplasia. Most CADe systems are developed with data from expert centers, unrepresentative of heterogeneous imaging conditions in community hospitals, and therefore may underperform in routine practice. We aimed to develop a robust CADe system (CADe 2.0) and compare its performance to a previously published system (CADe 1.0) under heterogeneous imaging conditions representative of real-world clinical practice.

Method CADe 2.0 was improved through a larger and more diverse training dataset, optimized pretraining, data augmentation, ground truth use, and architectural adjustments. CADe systems were evaluated using three prospective test sets. Test set 1 comprised 428 Barrett’s videos (114 patients across five referral centers). Test set 2 addressed endoscopist-dependent variation (e. g. mucosal cleaning and esophageal expansion), with paired subsets of high, moderate, and low quality images (122 patients). Test set 3 addressed endoscopist-independent variation, with 16 paired subsets of 396 images (122 patients), each being based on a different software image-enhancement setting.

Results CADe 2.0 outperformed CADe 1.0 on all three test sets. In test set 1, sensitivity increased significantly from 87 % to 96 % (P = 0.02), while specificity remained comparable (73 % vs. 74 %; P = 0.73). In test set 2, CADe 2.0 consistently surpassed CADe 1.0 across all image quality levels, with the largest performance gains observed on lower quality images (sensitivity 78 % vs. 61 %; specificity 89 % vs. 77 %; area under the curve 89 % vs. 75 %). In test set 3, CADe 2.0 showed improved performance and displayed reduced performance variability across enhancement settings.

Conclusion Based on several key improvements, CADe 2.0 demonstrated increased detection rates and better robustness to data heterogeneity, making it ready for clinical implementation.

‡ joint first authors


Supplementary Material



Publikationsverlauf

Eingereicht: 28. Januar 2025

Angenommen nach Revision: 22. Juni 2025

Accepted Manuscript online:
25. Juni 2025

Artikel online veröffentlicht:
19. August 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/)

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