Identifying Schizophrenia Using Structural MRI With a Deep Learning Algorithm.

ABSTRACT:

Objective

Although 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.

Method

Five 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.

Results

The 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

SUBMITTER: Oh J 

PROVIDER: S-EPMC7008229 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature