Evidence map›Paper›PMID 40562067›Full record

ArticleEndoscopy2025

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

Martijn R Jong, Rixta A H van Eijck van Heslinga, Carolus H J Kusters, Tim J M Jaspers, Tim G W Boers, Lucas C Duits, Roos E Pouw, Bas L A M Weusten, Alaa Alkhalaf, Fons van der Sommen and 4 more

Abstract readEvaluation StudyComparative StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Martijn R Jong *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 *Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Carolus H J KustersDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Tim J M JaspersDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Tim G W BoersDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Lucas C DuitsDepartment of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Roos E PouwDepartment of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Bas L A M WeustenDepartment of Gastroenterology and Hepatology, St Antonius Hospital, Nieuwegein, The Netherlands.ORCID 0000-0001-9468-4578
Alaa AlkhalafDepartment of Gastroenterology and Hepatology, Isala Hospital, Zwolle, The Netherlands.
Fons van der SommenDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Peter H N de WithDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Albert J de GroofDepartment of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Jacques J BergmanDepartment of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0001-7548-6955
BONSAI Consortium

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComputer-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.

methodCADe 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.

resultsCADe 2.0 outperformed CADe 1.0 on all three test sets. In test set 1, sensitivity increased significantly from 87 % to 96 % (

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

Indexed as

AdenocarcinomaBarrett EsophagusDiagnosis, Computer-AssistedEsophageal NeoplasmsEsophagoscopyImage Interpretation, Computer-AssistedAgedFemaleHumansMaleMiddle AgedProspective StudiesSensitivity and Specificity

Identifiers

PMID40562067
PMCPMC12659990

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.