Hilal Er Ulubaba, Rukiye Çiftçi, İpek Atik, Osman Furkan Karakuş
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引用次数: 0
Abstract
Purpose: 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.
期刊介绍:
Diagnostic and Interventional Radiology (Diagn Interv Radiol) is the open access, online-only official publication of Turkish Society of Radiology. It is published bimonthly and the journal’s publication language is English.
The journal is a medium for original articles, reviews, pictorial essays, technical notes related to all fields of diagnostic and interventional radiology.