An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future …

D Sadeghi, A Shoeibi, N Ghassemi, P Moridian… - Computers in Biology …, 2022 - Elsevier
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …

Evaluation of risk of bias in neuroimaging-based artificial intelligence models for psychiatric diagnosis: a systematic review

Z Chen, X Liu, Q Yang, YJ Wang, K Miao… - JAMA network …, 2023 - jamanetwork.com
Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …

[HTML][HTML] Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

Z Chen, B Hu, X Liu, B Becker, SB Eickhoff, K Miao… - BMC medicine, 2023 - Springer
Background The development of machine learning models for aiding in the diagnosis of
mental disorder is recognized as a significant breakthrough in the field of psychiatry …

Machine learning techniques for the Schizophrenia diagnosis: a comprehensive review and future research directions

S Verma, T Goel, M Tanveer, W Ding, R Sharma… - Journal of Ambient …, 2023 - Springer
Schizophrenia (SCZ) is a brain disorder where different people experience different
symptoms, such as hallucination, delusion, flat-talk, disorganized thinking, etc. In the long …

Magnetic resonance texture analysis reveals stagewise nonlinear alterations of the frontal gray matter in patients with early psychosis

SY Moon, H Park, W Lee, S Lee, SK Lho, M Kim… - Molecular …, 2023 - nature.com
Although gray matter (GM) abnormalities are present from the early stages of psychosis,
subtle/miniscule changes may not be detected by conventional volumetry. Texture analysis …

[HTML][HTML] Deep learning in neuroimaging data analysis: applications, challenges, and solutions

LK Avberšek, G Repovš - Frontiers in neuroimaging, 2022 - frontiersin.org
Methods for the analysis of neuroimaging data have advanced significantly since the
beginning of neuroscience as a scientific discipline. Today, sophisticated statistical …

[HTML][HTML] Gray level co-occurrence matrix and extreme learning machine for Alzheimer's disease diagnosis

S Gao - International Journal of Cognitive Computing in …, 2021 - Elsevier
Alzheimer's disease (AD) is a chronic neurodegenerative disease, which is one of the
biggest challenges in geriatrics. The global incidence of Alzheimer's disease has been on …

Automated rest eeg-based diagnosis of depression and schizophrenia using a deep convolutional neural network

Z Wang, J Feng, R Jiang, Y Shi, X Li, R Xue, X Du… - IEEE …, 2022 - ieeexplore.ieee.org
Depression (DP) and schizophrenia (SCZ) are both highly prevalent psychiatric disorders,
and their diagnosis depends on the examination of symptoms and clinical tests, which can …

[HTML][HTML] Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health-Are We Getting There?

A Solanes, J Radua - Frontiers in Psychiatry, 2022 - frontiersin.org
Schizophrenia is a mental disorder among the leading disabling conditions worldwide (1). It
affects approximately 20 million people and increases 2–3 times the probability of dying …

An effective diagnosis of schizophrenia using kernel ridge regression-based optimized RVFL classifier

SA Varaprasad, T Goel, M Tanveer, R Murugan - Applied Soft Computing, 2024 - Elsevier
Schizophrenia (SCZ) is a severe mental and debilitating neuropsychiatric disorder that
disrupts a person's thought processes, emotions, and behavior. Due to misdiagnosis, self …