Detection of common bile duct dilatation on magnetic resonance cholangiopancreatography by deep learning

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dc.contributor.authorUlubaba, Hilal Er
dc.contributor.authorCiftci, Rukiye
dc.contributor.authorAtik, Ipek
dc.contributor.authorKarakul, Osman Furkan
dc.date.accessioned2026-04-04T13:30:51Z
dc.date.available2026-04-04T13:30:51Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractPURPOSE This study aims to detect common bile duct (CBD) dilatation using deep learning methods from artificial intelligence algorithms. METHODS To create a convolutional neural network (CNN) model, 77 magnetic resonance cholangiopancreatography (MRCP) images without CBD dilatation and 70 MRCP images with CBD dilatation were used. The system was developed using coronal maximum intensity projection reformatted 3D-MRCP images. The ResNet50, DenseNet121, and visual geometry group models were selected for training, and detailed training was performed on each model. RESULTS In the study, the DenseNet121 model showed the best performance, with a 97% accuracy rate. The ResNet50 model ranked second, with a 96% accuracy rate. CONCLUSION CBD dilatation was detected with high performance using the DenseNet CNN model. Once validated in multicenter studies with larger datasets, this method may help in diagnosis and treatment decision-making. CLINICAL SIGNIFICANCE Deep learning algorithms can aid clinicians and radiologists in the diagnostic process once technical, ethical, and financial limitations are addressed. Fast and accurate diagnosis is crucial for accelerating treatment, reducing complications, and shortening hospital stays.
dc.identifier.doi10.4274/dir.2025.253218
dc.identifier.endpage538
dc.identifier.issn1305-3612
dc.identifier.issue6
dc.identifier.orcid0000-0003-2124-4525
dc.identifier.pmid40321102
dc.identifier.scopus2-s2.0-105021013210
dc.identifier.scopusqualityN/A
dc.identifier.startpage532
dc.identifier.trdizinid1355965
dc.identifier.urihttps://doi.org/10.4274/dir.2025.253218
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1355965
dc.identifier.urihttps://hdl.handle.net/11616/108415
dc.identifier.volume31
dc.identifier.wosWOS:001611828600001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherTurkish Soc Radiology
dc.relation.ispartofDiagnostic and Interventional Radiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectArtificial intelligence
dc.subjectbile duct dilatation
dc.subjectcholedocholithiasis
dc.subjectconvolutional neural network
dc.subjectmagnetic resonance cholangiopancreatography
dc.titleDetection of common bile duct dilatation on magnetic resonance cholangiopancreatography by deep learning
dc.typeArticle

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