Evidence map›Paper›PMID 39675349›Full record

ArticleDigestion2025

Deep Learning-Based Detection of Malignant Bile Duct Stenosis in Fluoroscopy Images of Endoscopic Retrograde Cholangiopancreatography.

Kien Vu Trung, Marcus Hollenbach, Gregory Patrick Veldhuizen, Oliver Lester Saldanha, Jakob Garbe, Jonas Rosendahl, Sebastian Krug, Patrick Michl, Jürgen Feisthammel, Thomas Karlas and 3 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Digestion, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

13 authors.

Kien Vu TrungDivision of Gastroenterology, Medical Department II, University of Leipzig Medical Center, Leipzig, Germany, kien.v.trung@gmail.com.
Marcus HollenbachDivision of Gastroenterology, Medical Department II, University of Leipzig Medical Center, Leipzig, Germany.
Gregory Patrick VeldhuizenElse Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
Oliver Lester SaldanhaElse Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
Jakob GarbeMedical Department I, Martin-Luther University Halle-Wittenberg, Halle, Germany.
Jonas RosendahlMedical Department I, Martin-Luther University Halle-Wittenberg, Halle, Germany.
Sebastian KrugMedical Department I, Martin-Luther University Halle-Wittenberg, Halle, Germany.
Patrick MichlMedical Department I, Martin-Luther University Halle-Wittenberg, Halle, Germany.
Jürgen FeisthammelDivision of Gastroenterology, Medical Department II, University of Leipzig Medical Center, Leipzig, Germany.
Thomas KarlasDivision of Gastroenterology, Medical Department II, University of Leipzig Medical Center, Leipzig, Germany.
Jochen HampeDepartment of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
Albrecht HoffmeisterDivision of Gastroenterology, Medical Department II, University of Leipzig Medical Center, Leipzig, Germany.
Jakob Nikolas KatherElse Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe accurate distinction between benign and malignant biliary strictures (BSs) poses a significant challenge. Despite the use of bile duct biopsies and brush cytology via endoscopic retrograde cholangiopancreaticography (ERCP), the results remain suboptimal. Single-operator cholangioscopy can enhance the diagnostic yield in BS, but its limited availability and high costs are substantial barriers. Convolutional neural network-based systems may improve the diagnostic process and enhance reproducibility. Therefore, we assessed the feasibility of using deep learning to differentiate BS using fluoroscopy images during ERCP.

methodsWe conducted a retrospective review of adult patients (n = 251) from three university centers in Germany (Leipzig, Dresden, Halle) who underwent ERCP. We developed and evaluated a deep learning-based model using fluoroscopy images. The performance of the classifier was evaluated by measuring the area under the receiver operating characteristic curve (AUROC), and we utilized saliency map analyses to understand the decision-making process of the model.

resultsIn cross-validation experiments, malignant BSs were detected with a mean AUROC of 0.89 ± 0.03. The test set of the Leipzig cohort demonstrated an AUROC of 0.90. In two independent external validation cohorts (Dresden, Halle), the deep learning-based classifier achieved an AUROC of 0.72 and 0.76, respectively. The artificial intelligence model's predictions identified plausible characteristics within the fluoroscopy images.

conclusionBy using a deep learning model, we were able to discriminate malignant BS from benign biliary conditions. The application of artificial intelligence enhances the diagnostic yield of malignant BS and should be validated in a prospective design.

Indexed as

Bile Duct NeoplasmsCholangiopancreatography, Endoscopic RetrogradeCholestasisDeep LearningAdultAgedAged, 80 and overBile DuctsConstriction, PathologicFeasibility StudiesFemaleFluoroscopyGermanyHumansMaleMiddle AgedArtificial intelligenceBile duct stenosisEndoscopic retrograde cholangiopancreaticographyEndoscopy

Identifiers

PMID39675349
PMCPMC12324797

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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.