Identifying Schizophrenia Using Structural MRI With a Deep Learning Algorithm
Identifying Schizophrenia Using Structural MRI With a Deep Learning Algorithm
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DOI：
10.3389/fpsyt.2020.00016
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发表时间：
2020-02-03
影响因子：
4.7
通讯作者：
Yun, Kyongsik
中科院分区：
文献类型：
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作者：
Oh, Jihoon;Oh, Baek-Lok;Yun, Kyongsik
目的尽管精神分裂症患者会出现明显的结构异常，但利用磁共振成像（MRI）检测精神分裂症仍然具有挑战性。本研究旨在使用经过训练的深度学习算法在结构MRI数据集中检测精神分裂症。方法使用来自精神分裂症患者和正常受试者的五个公共MRI数据集（BrainGluSchi、COBRE、MCICShare、NMorphCH和NUSDAST），总共873个结构MRI数据集来训练深度卷积神经网络。结果用检测到的结构MR图像训练深度学习算法随机选择的精神分裂症图像具有可靠的性能（受试者工作特征曲线下面积 [AUC] 为 0.96）。该算法还可以在以前未遇到的数据集中识别精神分裂症患者的 MR 图像，AUC 为 0.71 至 0.90。当呈现比训练数据集更年轻且病程更短的新数据集时，深度学习算法的分类性能下降至 AUC 0.71。对算法性能贡献最大的大脑区域是右颞区，其次是右顶叶区。在 100 张随机选择的大脑图像中，半训练有素的临床专家很难区分精神分裂症患者和健康对照（AUC：0.61）。结论深度学习算法在检测精神分裂症方面表现出良好的性能，并从结构性脑部 MRI 数据中识别出相关的结构特征；它在疾病早期阶段的另一组患者中具有可接受的分类性能。深度学习可用于描绘精神分裂症的结构特征，并在临床环境中提供补充诊断信息。
ObjectiveAlthough distinctive structural abnormalities occur in patients with schizophrenia, detecting schizophrenia with magnetic resonance imaging (MRI) remains challenging. This study aimed to detect schizophrenia in structural MRI data sets using a trained deep learning algorithm.MethodFive public MRI data sets (BrainGluSchi, COBRE, MCICShare, NMorphCH, and NUSDAST) from schizophrenia patients and normal subjects, for a total of 873 structural MRI data sets, were used to train a deep convolutional neural network.ResultsThe deep learning algorithm trained with structural MR images detected schizophrenia in randomly selected images with reliable performance (area under the receiver operating characteristic curve [AUC] of 0.96). The algorithm could also identify MR images from schizophrenia patients in a previously unencountered data set with an AUC of 0.71 to 0.90. The deep learning algorithm's classification performance degraded to an AUC of 0.71 when a new data set with younger patients and a shorter duration of illness than the training data sets was presented. The brain region contributing the most to the performance of the algorithm was the right temporal area, followed by the right parietal area. Semitrained clinical specialists hardly discriminated schizophrenia patients from healthy controls (AUC: 0.61) in the set of 100 randomly selected brain images.ConclusionsThe deep learning algorithm showed good performance in detecting schizophrenia and identified relevant structural features from structural brain MRI data; it had an acceptable classification performance in a separate group of patients at an earlier stage of the disease. Deep learning can be used to delineate the structural characteristics of schizophrenia and to provide supplementary diagnostic information in clinical settings.