BACKGROUND: Coronary artery disease (CAD) causes substantial death toll in the United States and worldwide. While traditional methods for CAD mortality prediction are based on established risk factors, they have significant limitations in accuracy, a...
OBJECTIVE: Post-stroke depression (PSD) is a common psychiatric complication following stroke, with low clinical detection rates and delayed diagnosis. Most existing PSD prediction models suffer from incomplete data inclusion, which limits their clin...
PURPOSE: To evaluate spectrum bias in stroke MRI analysis by excluding cases with uncertain acute ischemic lesions (AIL) and examining patient, imaging, and lesion factors associated with these cases.
OBJECTIVE: Identifying cognitive impairment early enough could support timely intervention of cognitive impairment and facilitate successful cognitive aging. We aimed to build more precise prediction models for cognitive function using less variable ...
BACKGROUND AND OBJECTIVES: Embolic protection devices (EPDs) used during carotid artery stenting (CAS) are crucial in reducing ischemic complications. Although minimizing the filter-type EPD movement is considered important, limited research has demo...
OBJECTIVES: Optimize deep learning-based vertebrae segmentation in longitudinal CT scans of multiple myeloma patients using structural uncertainty analysis.
Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson's disease heavily relied on T1-weighted imaging (T1WI) atlases. However, subcortical structures are characterized by subtle contrast differences, making their ...
BACKGROUND: Monitoring intrinsic capacity and implementing appropriate interventions can support healthy aging. There are, though, few tools available for predicting decline in intrinsic capacity among older adults. This study aimed to develop and va...
OBJECTIVE: We aimed to develop a machine learning (ML) model to preoperatively predict surgical difficulty for pheochromocytomas and paragangliomas (PPGLs) using clinical and radiomic features.
BACKGROUND: The exposome framework seeks to unravel the cumulated effects of environmental exposures on health. However, existing methods struggle with challenges including multicollinearity, non-linearity and confounding. To address these limitation...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.