ArticleEuropean radiology2021
Fully automated detection of primary sclerosing cholangitis (PSC)-compatible bile duct changes based on 3D magnetic resonance cholangiopancreatography using machine learning.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
- Identification of Primary Sclerosing Cholangitis: ICD-10 Code Validation and Comparison with a Large Language Model Approach.Digestive diseases and sciences · 2026Article
- Detection of common bile duct dilatation on magnetic resonance cholangiopancreatography by deep learning.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Exploring the Role of MRCP+ for Enhancing Detection of High-Grade Strictures in Primary Sclerosing Cholangitis.Journal of clinical medicine · 2025Article
- Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma.Scientific reports · 2025Article
- Deep Learning-Based Prediction of Hepatic Decompensation in Patients With Primary Sclerosing Cholangitis With Computed Tomography.Mayo Clinic proceedings. Digital health · 2024Article
- Primary Sclerosing Cholangitis: Diagnostic Criteria.Tomography (Ann Arbor, Mich.) · 2024Review
- DeePSC: A Deep Learning Model for Automated Diagnosis of Primary Sclerosing Cholangitis at Two-dimensional MR Cholangiopancreatography.Radiology. Artificial intelligence · 2023Article
- Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research.World journal of hepatology · 2021Review
- Artificial Intelligence in Hepatology- Ready for the Primetime.Journal of clinical and experimental hepatologyReview
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop and evaluate a deep learning algorithm for fully automated detection of primary sclerosing cholangitis (PSC)-compatible cholangiographic changes on three-dimensional magnetic resonance cholangiopancreatography (3D-MRCP) images.
methodsThe datasets of 428 patients (n = 205 with confirmed diagnosis of PSC; n = 223 non-PSC patients) referred for MRI including MRCP were included in this retrospective IRB-approved study. Datasets were randomly assigned to a training (n = 386) and a validation group (n = 42). For each case, 20 uniformly distributed axial MRCP rotations and a subsequent maximum intensity projection (MIP) were calculated, resulting in a training database of 7720 images and a validation database of 840 images. Then, a pre-trained Inception ResNet was implemented which was conclusively fine-tuned (learning rate 10
resultsApplying an ensemble strategy (by binning of the 20 axial projections), the mean absolute error (MAE) of the developed deep learning algorithm for detection of PSC-compatible cholangiographic changes was lowered from 21 to 7.1%. Sensitivity, specificity, positive predictive (PPV), and negative predictive value (NPV) for detection of these changes were 95.0%, 90.9%, 90.5%, and 95.2% respectively.
conclusionsThe results of this study demonstrate the feasibility of transfer learning in combination with extensive image augmentation to detect PSC-compatible cholangiographic changes on 3D-MRCP images with a high sensitivity and a low MAE. Further validation with more and multicentric data is now desirable, as it is known that neural networks tend to overfit the characteristics of the dataset. KEY POINTS: • The described machine learning algorithm is able to detect PSC-compatible cholangiographic changes on 3D-MRCP images with high accuracy. • The generation of 2D projections from 3D datasets enabled the implementation of an ensemble strategy to boost inference performance.
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