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Classification of Neuropsychiatric Disorders via Brain-Region-Selected Graph Convolutional Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
For the classification of patients with neuropsychiatric disorders based on rs-fMRI data, this paper proposed a Brain-Region-Selected graph convolutional network (BRS-GCN). In order to effectively identify the most significant biomarkers associated w...

Development and Validation of the Novel Exergame-Integrated Robotic Stepper Device for Seated Lower Limb Rehabilitation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Seated rehabilitation is essential in early-stage recovery for patients who can sit but cannot stand or walk. Robotic-based lower limb rehabilitation provides precise, task-specific training for recovery, but its application in seated exercises remai...

Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates.

Computers in biology and medicine
A cardiac digital twin is a virtual replica of a patient-specific heart, mimicking its anatomy and physiology. A crucial step of building a cardiac digital twin is anatomical twinning, where the computational mesh of the digital twin is tailored to t...

Use of posterior probabilities from a long short-term memory network for characterizing dance behavior with multiple accelerometers.

Journal of Alzheimer's disease : JAD
BackgroundDancing may be protective for cognitive health among adults with mild cognitive impairment, Alzheimer's disease or dementia; however, additional methods are needed to characterize motor behavior quality in studies of dance.ObjectiveTo deter...

PSMA PET/CT for prostate cancer diagnosis: current applications and future directions.

Journal of cancer research and clinical oncology
Prostate cancer (PCa) requires improved diagnostic strategies beyond conventional imaging. This review aimed to evaluate the role of prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) in diagnosing advan...

Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning.

Journal of substance use and addiction treatment
INTRODUCTION: Emergency departments (ED) are potential sites for identifying and treating individuals at high risk for opioid overdose. This study used machine learning (ML)-based models to predict opioid overdose death in the 12 months after an ED v...

Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study.

Medical physics
BACKGROUND: Patients with early-stage non-small cell lung cancer (NSCLC) typically receive surgery as their primary form of treatment. However, studies have shown that a high proportion of these patients will experience a recurrence after their resec...

Explainable machine learning model for prediction of 28-day all-cause mortality in immunocompromised patients in the intensive care unit: a retrospective cohort study based on MIMIC-IV database.

European journal of medical research
OBJECTIVES: This study aimed to develop and validate an explainable machine learning (ML) model to predict 28-day all-cause mortality in immunocompromised patients admitted to the intensive care unit (ICU). Accurate and interpretable mortality predic...

Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.

BMC medical imaging
OBJECTIVE: Pituitary adenomas (PAs), craniopharyngiomas (CRs), Rathke's cleft cysts (RCCs), and tuberculum sellar meningiomas (TSMs) are common sellar region lesions with similar imaging characteristics, making differential diagnosis challenging. Thi...

Effects of exoskeleton rehabilitation robot training on neuroplasticity and lower limb motor function in patients with stroke.

BMC neurology
BACKGROUND: Lower limb exoskeleton rehabilitation robot is a new technology to improve the lower limb motor function of stroke patients. Recovery of motor function after stroke is closely related to neuroplasticity in the motor cortex and associated ...