BACKGROUND: Depression is the top contributor to global disability. Early detection of depression and depressive symptoms enables timely intervention and reduces their physical and social consequences. Prevalence estimates of depression approach 30% ...
Journal of neuroengineering and rehabilitation
May 25, 2025
BACKGROUND: Major Depressive Disorder is a leading cause of disability worldwide. An accurate assessment of depression severity is critical for diagnosis, treatment planning, and monitoring, yet current clinical tools are largely subjective, relying ...
Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tackle this, we developed a cloud-based computer-aided depression diagnostic (CCADD) system that utiliz...
IEEE journal of biomedical and health informatics
May 6, 2025
Depression, a prevalent mental health disorder with severe health and economic consequences, can be costly and difficult to detect. To alleviate this burden, recent research has been exploring the depression screening capabilities of deep learning (D...
Traditional clinical risk assessment tools proved inadequate for reliably identifying individuals at high risk for suicidal behavior. As a result, machine learning (ML) techniques have become progressively incorporated into psychiatric care. This stu...
INTRODUCTION: Perinatal depression and anxiety (PDA) is associated with a high risk of maternal mortality. Existing data shows that 95% of maternal mortality in low- and middle-income countries (LMICs) is due to resource constraints and negligence in...
Depression during pregnancy and postpartum poses significant risks to both maternal and child well-being. The underlying biological mechanisms are unclear, but epigenetic variation could be exploited as a plausible candidate for early detection. We i...
Depression is highly prevalent among patients with chronic disease advanced and with poor clinical outcomes. However, effective tools for identifying individuals at risk remain limited. This study aimed to develop and validate a predictive model for ...
Journal of magnetic resonance imaging : JMRI
Apr 22, 2025
BACKGROUND: Meta-analyses in neuroimaging have gained popularity. However, their clinical utility remains uncertain. Convergent masks, containing repeated clusters from publications, are often focal and small, and voxel-wise features can lead to the ...
OBJECTIVE: Artificial neural networks have been used in various fields to solve classification and prediction tasks. However, it is unclear if these may be adequate methods to predict psychological treatment outcomes. This study aimed to evaluate the...
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