Intracerebral hemorrhage (ICH) has one of the worst prognoses among patients with stroke.
Surgical measures have been adopted to relieve the mass effect of the hematoma, and …

The application of machine learning has rapidly evolved in medicine over the past decade.
In stroke, commercially available machine learning algorithms have already been …

Background Artificial intelligence (AI)-driven software has been developed and become
commercially available within the past few years for the detection of intracranial hemorrhage …

Objectives To evaluate for the first time the performance of a deep learning method based
on no-new-Net for fully automated segmentation and volumetric measurements of …

The rapid development of computational methods and the increasing volume of chemical
and biological data have contributed to an immense growth in chemical research. This field …

Acute intracerebral hemorrhage (ICH) is a devastating type of stroke worldwide. Neuronal
destruction involved in the brain damage process caused by ICH includes a primary injury …

To investigate the performance of a joint convolutional neural networks-recurrent neural
networks (CNN-RNN) using an attention mechanism in identifying and classifying …

Intracerebral hemorrhage (ICH) is the most common type of hemorrhagic stroke which
occurs due to ruptures of weakened blood vessel in brain tissue. It is a serious medical …

Objective To devise and validate radiomic signatures of impending hematoma expansion
(HE) based on admission non-contrast head computed tomography (CT) of patients with …

This study evaluated deep learning algorithms for semantic segmentation and quantification
of intracerebral hemorrhage (ICH), perihematomal edema (PHE), and intraventricular …