Results 71 to 80 of about 71,222 (318)
Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier
There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially available ...
H. Kaka, Euan Zhang, Nazir Khan
semanticscholar +1 more source
Objective Spinal cord (SC) atrophy correlates with and predicts the underlying progressive biology in active and non‐active multiple sclerosis (MS), thereby providing a biomarker for clinical trials and patient management. Initiation of disease‐modifying therapy (DMT) may be followed by early pronounced central nervous system (CNS) volume loss due to ...
Simone Sacco +26 more
wiley +1 more source
Promises of artificial intelligence in neuroradiology: a systematic technographic review
To conduct a systematic review of the possibilities of artificial intelligence (AI) in neuroradiology by performing an objective, systematic assessment of available applications.
A. Olthof +2 more
semanticscholar +1 more source
Objective The efficacy and safety of intra‐arterial thrombolysis (IAT) as an adjunct to endovascular thrombectomy (EVT) in large vessel occlusion strokes (LVOS) remain uncertain, with recent randomized controlled trials (RCTs) yielding conflicting results.
Mohamed F. Doheim +20 more
wiley +1 more source
Objective Impaired ability to induce stepping after incomplete spinal cord injury (SCI) can limit the efficacy of locomotor training, often leaving patients wheelchair‐bound. The cuneiform nucleus (CNF), a key mesencephalic locomotor control center, modulates the activity of spinal locomotor centers via the reticulospinal tract.
Anna‐Sophie Hofer +21 more
wiley +1 more source
Clinical–Radiological Spectrum of Cerebral Amyloid Angiopathy‐Related Inflammation
Objective To identify clinical and radiological features of cerebral amyloid angiopathy‐related inflammation (CAA‐ri), and compare these features with those of sporadic CAA, to improve the understanding, diagnosis, and clinical care of CAA‐ri. Methods We retrospectively reviewed routine clinical data from 37 patients with CAA‐ri and 158 patients with ...
Larysa Panteleienko +9 more
wiley +1 more source
Deep Learning in Neuroradiology
SUMMARY: Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications.
G. Zaharchuk +4 more
semanticscholar +1 more source
Essential Components of Child Neurology Training: Program Director Consensus Recommendations
ABSTRACT Objective We aimed to develop a program director–derived model of essential components of child neurology residency training. Methods All 79 child neurology residency programs in the United States were invited to submit a block diagram with 48 months of required rotations, the minimum clinical requirement across all approved pathways.
Danny Rogers +36 more
wiley +1 more source
Quantitative Evaluation of Performance in Interventional Neuroradiology: An Integrated Curriculum Featuring Theoretical and Practical Challenges. [PDF]
We sought to develop a standardized curriculum capable of assessing key competencies in Interventional Neuroradiology by the use of models and simulators in an objective, quantitative, and efficient way.
Marielle Ernst +6 more
doaj +1 more source
SUMMARY: Identification of carotid artery atherosclerosis is conventionally based on measurements of luminal stenosis and surface irregularities using in vivo imaging techniques including sonography, CT and MR angiography, and digital subtraction ...
L. Saba +18 more
semanticscholar +1 more source

