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

IF 1.7 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
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.

磁共振胆管造影深度学习检测胆总管扩张。
目的:本研究旨在利用人工智能算法中的深度学习方法检测胆总管(CBD)扩张。方法:选取77张无CBD扩张的磁共振胆管造影(MRCP)图像和70张有CBD扩张的MRCP图像,建立卷积神经网络(CNN)模型。该系统是利用冠状最大强度投影重新格式化的3D-MRCP图像开发的。选择ResNet50、DenseNet121和视觉几何组模型进行训练,并对每个模型进行详细训练。结果:在本研究中,DenseNet121模型表现最佳,准确率为97%。ResNet50模型排名第二,准确率为96%。结论:DenseNet CNN模型能较好地检测CBD舒张。一旦在更大数据集的多中心研究中得到验证,该方法可能有助于诊断和治疗决策。临床意义:一旦解决了技术、伦理和财务限制,深度学习算法可以在诊断过程中帮助临床医生和放射科医生。快速准确的诊断对于加快治疗、减少并发症和缩短住院时间至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Diagnostic and interventional radiology
Diagnostic and interventional radiology Medicine-Radiology, Nuclear Medicine and Imaging
自引率
4.80%
发文量
0
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.
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