Convolutional neural networks for radiologic images: a radiologist's guide
S Soffer, A Ben-Cohen, O Shimon, MM Amitai… - Radiology, 2019 - pubs.rsna.org
Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …
recently gained particular attention in the radiology community. This article provides an …
Artificial intelligence and acute stroke imaging
JE Soun, DS Chow, M Nagamine… - American Journal …, 2021 - Am Soc Neuroradiology
Artificial intelligence technology is a rapidly expanding field with many applications in acute
stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute …
stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute …
Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning
Background and Purpose—Treatment options for patients with acute ischemic stroke
depend on the volume of salvageable tissue. This volume assessment is currently based on …
depend on the volume of salvageable tissue. This volume assessment is currently based on …
Prevalence and diagnosis of neurological disorders using different deep learning techniques: a meta-analysis
This paper dispenses an exhaustive review on deep learning techniques used in the
prognosis of eight different neuropsychiatric and neurological disorders such as stroke …
prognosis of eight different neuropsychiatric and neurological disorders such as stroke …
Use of deep learning to predict final ischemic stroke lesions from initial magnetic resonance imaging
Importance Predicting infarct size and location is important for decision-making and
prognosis in patients with acute stroke. Objectives To determine whether a deep learning …
prognosis in patients with acute stroke. Objectives To determine whether a deep learning …
Artificial intelligence applications in stroke
K Mouridsen, P Thurner, G Zaharchuk - Stroke, 2020 - Am Heart Assoc
Management of stroke highly depends on informa-tion from imaging studies. Noncontrast
computed tomography (CT) and magnetic resonance imaging (MRI) can both be used to …
computed tomography (CT) and magnetic resonance imaging (MRI) can both be used to …
[HTML][HTML] Functional outcome prediction in ischemic stroke: a comparison of machine learning algorithms and regression models
SA Alaka, BK Menon, A Brobbey, T Williamson… - Frontiers in …, 2020 - frontiersin.org
Background and Purpose: Stroke-related functional risk scores are used to predict patients'
functional outcomes following a stroke event. We evaluate the predictive accuracy of …
functional outcomes following a stroke event. We evaluate the predictive accuracy of …
Deep learning guided stroke management: a review of clinical applications
R Feng, M Badgeley, J Mocco… - Journal of …, 2018 - jnis.bmj.com
Stroke is a leading cause of long-term disability, and outcome is directly related to timely
intervention. Not all patients benefit from rapid intervention, however. Thus a significant …
intervention. Not all patients benefit from rapid intervention, however. Thus a significant …
An integrative paradigm for enhanced stroke prediction: Synergizing xgboost and xdeepfm algorithms
Stroke prediction plays a crucial role in preventing and managing this debilitating condition.
In this study, we address the challenge of stroke prediction using a comprehensive dataset …
In this study, we address the challenge of stroke prediction using a comprehensive dataset …
[HTML][HTML] A review on computer aided diagnosis of acute brain stroke
Amongst the most common causes of death globally, stroke is one of top three affecting over
100 million people worldwide annually. There are two classes of stroke, namely ischemic …
100 million people worldwide annually. There are two classes of stroke, namely ischemic …