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 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. Conclusions: The 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.

New Insights from Old Data: Multimodal Classification of Schizophrenia using Automated Deep Learning Configurations

“…The evolution of machine learning has contributed significantly to the healthcare industry through image detection, classification, and segmentation tasks but with respect to interest in mental health, there has been massive transformation specially with non-invasive neuroimaging[18]. For schizophrenia, in particular, machine learning methods[43] have been explored for diagnostics and classification from structural MRI[44] and functional MRI features[45], EEG signals[11] and resting EEG streams[4].…”

Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

“…Generally, neuroimaging methods include various structural or functional modalities (Steardo et al, 2020 ; Hu et al, 2021 ). Structural MRI and diffusion tensor imaging-MRI are among the most important modalities of structural neuroimaging, providing important information regarding brain structure to specialist physicians (Sui et al, 2013 ; Lee et al, 2018 ; Oh et al, 2020 ). Contrarily, electroencephalography (EEG) (Boutros et al, 2008 ), magnetoencephalography (Fernández et al, 2011 ), functional MRI (Sartipi et al, 2020 ), and functional near-infrared spectroscopy (Chen et al, 2020 ) are the most important functional modalities of the brain.…”

Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

“…In the radiological research of the human brain, the raw data is mainly the collected magnetic resonance images of the human brain. The main imaging methods include T1-weighted imaging, T2-weighted imaging, diffusion tensor imaging In the diagnosis of human brain-related diseases, such as AD (Alzheimer s disease) [13], PD (Parkinson's Disease) [14], MDD (major depressive disorder) [15], SCZ (schizophrenia) [16], ADHD (attention-deficit/hyperactivity disorder) [17], ASD (autism spectrum disorder) [17,18], etc., the use of AI methods to diagnose diseases has achieved satisfactory results [19]. In the radiological research of the human brain, the raw data is mainly the collected magnetic resonance images of the human brain.…”