Structural and diffusion MRI based schizophrenia classification using 2D pretrained and 3D naive Convolutional Neural Networks

Hu, Mengjiao; Qian, Xing; Liu, Siwei; Koh, Amelia Jialing; Sim, Kang; Jiang, Xudong; Guan, Cuntai; Zhou, Juan

doi:10.1016/j.schres.2021.06.011

Cited by 37 publications

(24 citation statements)

References 65 publications

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“… b Eighty-six AI models 30 , 32 , 34 , 36 , 42 , 44 , 46 , 53 , 59 , 60 , 61 , 63 , 69 , 70 , 72 , 81 , 82 , 88 , 91 , 117 , 149 , 151 , 156 , 162 , 163 , 164 , 169 , 170 , 172 , 180 , 182 , 187 , 191 , 197 , 212 , 213 , 220 , 221 , 247 , 254 , 258 , 260 , 261 , 263 , 269 , 272 , 278 , 280 , 283 , 284 , 285 , 287 , 290 , 301 , 305 , 307 , 319 , …”

Section: Methodsunclassified

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

et al. 2023

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ImportanceNeuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.ObjectiveTo systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.Evidence ReviewPubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.FindingsA total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).Conclusions and RelevanceThis systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

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Section: Methodsunclassified

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

et al. 2023

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“…In the present study, our classification algorithm was evaluated using eight independent samples to ensure generalisability. We found that the average accuracy over the eight samples marginally outperformed existing reports that included an independent sample for validation and used SVM classifiers (70.4-77% in 15,22 ) or deep-learning approaches (70.0-71.02% in [9][10][11][12] ).…”

Section: Interpretation Of Our Findings and Comparison With Other Stu...mentioning

confidence: 73%

“…To our knowledge, only a few studies have investigated independent replications using neuroanatomical features for the identification of schizophrenia with deep learning, and each of them reported lower accuracies when using a different validation sample than when using a single sample. 9–12 For instance, Oh and colleagues trained 3D CNNs and obtained 70% accuracy in a completely independent data-set. 9 Previous studies used either shallow 9 or deep 12 CNN architectures, but both had a poorer performance when using a completely different data-set for validation.…”

Section: Use Of Deep-learning Techniquesmentioning

confidence: 99%

“…9–12 For instance, Oh and colleagues trained 3D CNNs and obtained 70% accuracy in a completely independent data-set. 9 Previous studies used either shallow 9 or deep 12 CNN architectures, but both had a poorer performance when using a completely different data-set for validation. This may be because the number of samples (hundreds) is relatively small compared with the input of high-dimensional image data with a large number of voxels (hundreds of thousands) in CNN models; this can increase the risk of overfitting.…”

Section: Use Of Deep-learning Techniquesmentioning

confidence: 99%

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Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks

Cui

Liu

et al. 2022

Br J Psychiatry

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Background Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia. Aims To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers. Method We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites. Results We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19–85.74%; sensitivity, 75.31–89.29% and area under the receiver operating characteristic curve, 0.797–0.909. Conclusions These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.

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“…Numerous methods have been provided for automated SZ diagnosis; among these techniques, neuroimaging-based methods have a special potential for specialist physicians (Li et al, 2021 ; Yan et al, 2021 ). 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 ).…”

Section: Introductionmentioning

confidence: 99%

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

Shoeibi

Sadeghi

Moridian

et al. 2021

Front. Neuroinform.

117

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Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1D-CNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that to perform all simulations, the k-fold cross-validation method with k = 5 has been used.

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Structural and diffusion MRI based schizophrenia classification using 2D pretrained and 3D naive Convolutional Neural Networks

Cited by 37 publications

References 65 publications

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks

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

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