Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

S Vatansever, A Schlessinger, D Wacker… - Medicinal research …, 2021 - Wiley Online Library
Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, develo** drugs for central nervous system (CNS) disorders remains the most …

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 …

[HTML][HTML] Artificial intelligence for brain diseases: A systematic review

A Segato, A Marzullo, F Calimeri, E De Momi - APL bioengineering, 2020 - pubs.aip.org
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for
analyzing complex medical data and extracting meaningful relationships in datasets, for …

[HTML][HTML] One size does not fit all: methodological considerations for brain-based predictive modeling in psychiatry

E Dhamala, BTT Yeo, AJ Holmes - Biological Psychiatry, 2023 - Elsevier
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way
across individuals, and no two patients with a shared diagnosis exhibit identical symptom …

Challenges and future prospects of precision medicine in psychiatry

M Manchia, C Pisanu, A Squassina… - Pharmacogenomics …, 2020 - Taylor & Francis
Precision medicine is increasingly recognized as a promising approach to improve disease
treatment, taking into consideration the individual clinical and biological characteristics …

A systematic review and narrative synthesis of data-driven studies in schizophrenia symptoms and cognitive deficits

TD Habtewold, LH Rodijk, EJ Liemburg… - Translational …, 2020 - nature.com
To tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often
applied to identify subtypes of its symptoms and cognitive deficits. However, a systematic …

Towards artificial intelligence in mental health: a comprehensive survey on the detection of schizophrenia

A Tyagi, VP Singh, MM Gore - Multimedia Tools and Applications, 2023 - Springer
Abstract Computer Aided Diagnosis systems assist radiologists and doctors in the early
diagnosis of mental disorders such as Alzheimer's, bipolar disorder, depression, autism …

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

F Eitel, MA Schulz, M Seiler, H Walter, K Ritter - Experimental Neurology, 2021 - Elsevier
By promising more accurate diagnostics and individual treatment recommendations, deep
neural networks and in particular convolutional neural networks have advanced to a …

Map** Brain Synergy Dysfunction in Schizophrenia: Understanding Individual Differences and Underlying Molecular Mechanisms

C Ding, A Li, S **e, X Tian, K Li, L Fan, H Yan… - Advanced …, 2024 - Wiley Online Library
To elucidate the brain‐wide information interactions that vary and contribute to individual
differences in schizophrenia (SCZ), an information‐resolved method is employed to …

Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review

M Montazeri, M Montazeri… - Health science …, 2023 - Wiley Online Library
Abstract Background and Aim Schizophrenia and bipolar disorder (BD) are critical and high‐
risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population …