Stroke is an emergency in which delays in treatment can lead to significant loss of
neurological function and be fatal. Technologies that increase the speed and accuracy of …

Background and objective Acute ischemic stroke (AIS) is a common neurological disorder
characterized by the sudden onset of cerebral ischemia, leading to functional impairments …

Abstract Machine learning techniques for clinical applications are evolving, and the potential
impact this will have on clinical neurology is important to recognize. By providing a broad …

Background Although it is well known that the disordered brain provokes cardiac autonomic
dysfunction, the detailed location of brain lesions related to cardiac function warrants further …

The last decade has seen significant advances in the accumulation of medical data, the
computational techniques to analyze that data, and corresponding improvements in …

Purpose Automated lesion segmentation is increasingly used in acute ischemic stroke
magnetic resonance imaging (MRI). We explored in detail the performance of apparent …

This study aimed to develop and validate machine learning (ML) models that predict age
using intracranial vessels' tortuosity and diameter features derived from magnetic resonance …

Background To evaluate the effect of the weighting of input imaging combo and ADC
threshold on the performance of the U-Net and to find an optimized input imaging combo …

Background Constipation symptoms are highly prevalent in acute ischemic stroke, but the
clinical and neuroimaging predictors are unknown. This study aimed to identify lesions and …

Background Deep learning-based artificial intelligence techniques have been developed for
automatic segmentation of diffusion-weighted magnetic resonance imaging (DWI) lesions …