Aims Major depression disorder (MDD) is the single greatest cause of disability and
morbidity, and affects about 10% of the population worldwide. Currently, there are no …

Neuroimaging‐based approaches have been extensively applied to study mental illness in
recent years and have deepened our understanding of both cognitively healthy and …

Abstract Machine learning techniques provide new methods to predict diagnosis and clinical
outcomes at an individual level. We aim to review the existing literature on the use of …

Background Bipolar disorder (BD) is the 10th most common cause of frailty in young
individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life …

Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …

Artificial intelligence (AI) has been successfully exploited in diagnosing many mental
disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI …

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 …

Objective The frontal-limbic circuit is hypothesized as sub-serving emotional regulation. We
performed whole brain resting-state functional connectivity (rs-FC) analysis by studying the …

Objectives The existence of anatomofunctional brain abnormalities in bipolar disorder (BD)
is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic …

Applying machine learning (ML) to objective markers may overcome prognosis uncertainty
due to the subjective nature of the diagnosis of bipolar disorder (BD). This PRISMA …