Evidence map›Paper›PMID 39884307›Full record

ArticleEndoscopy2025

Impact of standard enhancement settings of endoscopy systems on performance of endoscopic artificial intelligence systems.

Martijn R Jong, Carolus H J Kusters, Querijn N E van Bokhorst, Jelmer B Jukema, Rixta A H van Eijck van Heslinga, Kiki N Fockens, Britt B S L Houwen, Tim J M Jaspers, Tim G W Boers, Manon van der Vlugt and 6 more

Abstract read
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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. Article
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

16 authors.

Martijn R Jong *Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Carolus H J Kusters *Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Querijn N E van BokhorstDepartment of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Jelmer B JukemaDepartment of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Rixta A H van Eijck van HeslingaDepartment of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Kiki N FockensDepartment of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Britt B S L HouwenDepartment of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Tim J M JaspersDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Tim G W BoersDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Manon van der VlugtDepartment of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Evelien DekkerDepartment of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0002-4363-0745
Fons van der SommenDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Peter H N de WithDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Albert J de GroofDepartment of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Jacques J G H M BergmanDepartment of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0001-7548-6955
BONS-AI Consortium

Funding

Nederlandse Organisatie voor Wetenschappelijk Onderzoek DANAE Project supported by NWO/KWF
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) systems in endoscopy are predominantly developed and tested using high-quality imagery from expert centers. However, their performance may be different when applied in clinical practice, partly due to the diversity in post-processing enhancement settings used in endoscopy units. We evaluated the impact of post-processing enhancement settings on AI performance and tested specific data augmentation strategies to mitigate performance loss.

methodsWe used a computer-aided detection (CADe) system for Barrett's neoplasia (6223 images, 906 patients) and a computer-aided diagnosis (CADx) system for colorectal polyps (3288 images, 969 patients), both trained on datasets acquired with Olympus equipment and with limited variability in enhancement settings. The CAD systems were then tested across a wide range of test sets, which comprised the same images, but displayed with different enhancement settings. Both CAD systems were then retrained using image enhancement-based data augmentation. The performance of the adjusted CAD systems was evaluated on the same test sets.

resultsBoth systems displayed substantial performance variability over a range of enhancement settings (CADe: 83 %-92 % sensitivity, 84 %-91 % specificity; CADx: 78 %-85 % sensitivity, 45 %-63 % specificity). After retraining, variability in sensitivity and specificity was reduced to 2 % (

conclusionThe performance of endoscopic AI systems can vary substantially depending on post-processing enhancement settings of the endoscopy unit. Specific data augmentation can mitigate this performance loss.

Indexed as

Artificial IntelligenceBarrett EsophagusColonic PolypsColonoscopyColorectal NeoplasmsDiagnosis, Computer-AssistedImage EnhancementHumansSensitivity and Specificity

Identifiers

PMID39884307
PMCPMC12119143

What OpenQuestion holds

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Registered trials

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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.