INTRODUCTION: Schizophrenia is a complex mental disorder whose pathophysiology remains elusive, particularly in the roles of subcortex. This study aims to explore the role of subcortex and insula and their relationship with symptom changes in first-e...
OBJECTIVE: Facial images have been shown to convey mental conditions as clinical symptoms. This study aimed to use facial images to detect patients with drug-naive schizophrenia (DN-SCZ) or chronic schizophrenia (C-SCZ) from healthy controls (HCs), a...
Skorobogatov et al. developed supervised machine learning models to predict diagnoses and illness states in schizophrenia and bipolar disorder. However, their reliance on bootstrap forests and generalized regressions introduces significant biases in ...
Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from...
In a letter critiquing our manuscript, Takefuji highlights general pitfalls in machine learning, without directly engaging with our study. The comments provide generic advice rather than a specific critique of our methods or findings. Despite raising...
Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characteriz...
International journal of molecular sciences
39684694
Mental health disorders are devastating illnesses, often misdiagnosed due to overlapping clinical symptoms. Among these conditions, bipolar disorder, schizophrenia, and schizoaffective disorder are particularly difficult to distinguish, as they share...
Identifying optimal treatment approaches for schizophrenia is challenging due to varying symptomatology and treatment responses. Artificial intelligence (AI) shows promise in predicting outcomes, prompting this systematic review and meta-analysis to ...
OBJECTIVE: The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted smartphone application, which records the medication ingestion event.
Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep learning-based AI algorithms are applied. The successful combination of different brain imaging mod...