Key result
Deep learning models automatically detect schizophrenia from EEG and MRI data without feature engineering.
Why the study?
Deep learning models have been increasingly applied for automated detection of schizophrenia using various signal data, but a comprehensive review of these approaches over the last decade was needed.
Can deep learning models accurately detect schizophrenia using EEG and MRI data?
Systematic Review
Can deep learning models accurately detect schizophrenia using EEG and MRI data?
Deep learning models offer a promising approach for the automated detection of schizophrenia using neuroimaging and electrophysiological data without the need for manual feature engineering.
DL models enable automated SZ detection from EEG/MRI without feature engineering; leaves open standardized validation before clinical adoption.
Schizophrenia (SZ) is a devastating mental disorder that disrupts higher brain functions like thought, perception, etc., with a profound impact on the individual's life. Deep learning (DL) can detect SZ automatically by learning signal data characteristics hierarchically without the need for feature engineering associated with traditional machine learning. We performed a systematic review of DL models for SZ detection. Various deep models like long short-term memory, convolution neural networks, AlexNet, etc., and composite methods have been published based on electroencephalographic signals, and structural and/or functional magnetic resonance imaging acquired from SZ patients and healthy patients control subjects in diverse public and private datasets. The studies, the study datasets, and model methodologies are reported in detail. In addition, the challenges of DL models for SZ diagnosis and future works are discussed.
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Sharma et al. (2023) conducted a systematic review in Schizophrenia. Deep learning models was evaluated on Schizophrenia detection. Deep learning models, including long short-term memory and convolution neural networks, can automatically detect schizophrenia using EEG and MRI data without the need for feature engineering.
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