Golla Alena-K, Tönnes Christian, Russ Tom, Bauer Dominik F, Froelich Matthias F, Diehl Steffen J, Schoenberg Stefan O, Keese Michael, Schad Lothar R, Zöllner Frank G, Rink Johann S
Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany.
Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany.
Diagnostics (Basel). 2021 Nov 17;11(11):2131. doi: 10.3390/diagnostics11112131.
Abdominal aortic aneurysms (AAA) may remain clinically silent until they enlarge and patients present with a potentially lethal rupture. This necessitates early detection and elective treatment. The goal of this study was to develop an easy-to-train algorithm which is capable of automated AAA screening in CT scans and can be applied to an intra-hospital environment. Three deep convolutional neural networks (ResNet, VGG-16 and AlexNet) were adapted for 3D classification and applied to a dataset consisting of 187 heterogenous CT scans. The 3D ResNet outperformed both other networks. Across the five folds of the first training dataset it achieved an accuracy of 0.856 and an area under the curve (AUC) of 0.926. Subsequently, the algorithms performance was verified on a second data set containing 106 scans, where it ran fully automated and resulted in an accuracy of 0.953 and an AUC of 0.971. A layer-wise relevance propagation (LRP) made the decision process interpretable and showed that the network correctly focused on the aortic lumen. In conclusion, the deep learning-based screening proved to be robust and showed high performance even on a heterogeneous multi-center data set. Integration into hospital workflow and its effect on aneurysm management would be an exciting topic of future research.
腹主动脉瘤(AAA)在临床上可能一直无症状,直到其扩大,患者出现可能致命的破裂。这就需要早期检测和择期治疗。本研究的目的是开发一种易于训练的算法,该算法能够在CT扫描中自动进行AAA筛查,并可应用于医院内部环境。三个深度卷积神经网络(ResNet、VGG - 16和AlexNet)被改编用于三维分类,并应用于一个由187幅异构CT扫描组成的数据集。三维ResNet的表现优于其他两个网络。在第一个训练数据集的五次交叉验证中,它的准确率达到0.856,曲线下面积(AUC)为0.926。随后,在包含106幅扫描图像的第二个数据集上验证了该算法的性能,该算法在该数据集上完全自动化运行,准确率达到0.953,AUC为0.971。逐层相关性传播(LRP)使决策过程具有可解释性,并表明该网络正确地聚焦于主动脉腔。总之,基于深度学习的筛查被证明是稳健的,即使在异构多中心数据集上也表现出高性能。将其集成到医院工作流程及其对动脉瘤管理的影响将是未来研究的一个令人兴奋的课题。