深度学习区分局灶性癫痫患者和对照组的连接体:可行性和临床意义

Christina Maher, Zihao Tang, Arkiev D’Souza, Mariano Cabezas, Weidong Cai, Michael Barnett, Omid Kavehei, Chenyu Wang, Armin Nikpour
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引用次数: 0

摘要

应用深度学习模型评估连接体数据在癫痫研究中越来越受到关注。深度学习可能是将连接组数据划分为网络子集以供进一步分析的有用初始工具。以前很少有研究使用深度学习来检查局灶性癫痫患者的结构连接体。我们评估了应用于全脑连接体的深度学习模型是否可以从20个对照组中对28名局灶性癫痫患者进行分类,并确定每组的节点重要性。癫痫患者根据是否有局灶性发作演变为双侧强直阵挛发作(17例有,11例无)进一步分组。训练后的神经网络对患者和对照组的分类准确率为72.92%,对癫痫亚型组的分类准确率为67.86%。在患者亚组中,被认为对准确分类很重要的节点和边缘也具有临床相关性,表明该模型具有可解释性。目前的工作扩展了深度学习从临床数据集中提取相关标记物的潜力的证据。我们的发现为进一步研究结构连接体以获得可作为生物标志物的特征并帮助诊断癫痫亚型提供了理论依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning distinguishes connectomes from focal epilepsy patients and controls: feasibility and clinical implications
Abstract The application of deep learning models to evaluate connectome data is gaining interest in epilepsy research. Deep learning may be a useful initial tool to partition connectome data into network subsets for further analysis. Few prior works have used deep learning to examine structural connectomes from patients with focal epilepsy. We evaluated whether a deep learning model applied to whole-brain connectomes could classify 28 participants with focal epilepsy from 20 controls and identify nodal importance for each group. Participants with epilepsy were further grouped based on whether they had focal seizures that evolved into bilateral tonic-clonic seizures (17 with, 11 without). The trained neural network classified patients from controls with an accuracy of 72.92%, while the seizure subtype groups achieved a classification accuracy of 67.86%. In the patient subgroups, the nodes and edges deemed important for accurate classification were also clinically relevant, indicating the model’s interpretability. The current work expands the evidence for the potential of deep learning to extract relevant markers from clinical datasets. Our findings offer a rationale for further research interrogating structural connectomes to obtain features that can be biomarkers and aid the diagnosis of seizure subtypes.
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