The use of artificial intelligence, and the deep-learning subtype in particular, has been
enabled by the use of labeled big data, along with markedly enhanced computing power …

Artificial intelligence technology is a rapidly expanding field with many applications in acute
stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute …

Objectives To evaluate the performance of a novel three-dimensional (3D) joint
convolutional and recurrent neural network (CNN-RNN) for the detection of intracranial …

Traumatic brain injuries may cause intracranial hemorrhages (ICH). ICH could lead to
disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure …

Objective To assess the diagnostic performance of a deep learning-based algorithm for
automated detection of acute and chronic rib fractures on whole-body trauma CT. Materials …

Abstract A 3-dimensional (3D) convolutional neural network is presented for the
segmentation and quantification of spontaneous intracerebral haemorrhage (ICH) in non …

We propose a novel method that combines a convolutional neural network (CNN) with a
long short-term memory (LSTM) mechanism for accurate prediction of intracranial …

The task of segmentation of brain regions affected by ischemic stroke is help to tackle
important challenges of modern stroke imaging analysis. Unfortunately, at the moment, the …

Transcranial ultrasound therapy is increasingly used for the non-invasive treatment of brain
disorders. However, conventional numerical wave solvers are currently too computationally …

Traumatic brain injury is a complex and diverse medical condition with a high frequency of
intracranial abnormalities. These can typically be visualized on a computed tomography …