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CT radiomics unlocks basal ganglia stroke onset time
The combination of radiomics and a machine-learning algorithm can determine...Read more on AuntMinnie.comRelated Reading: AI may help improve management of stroke patients AI finds infarction in stroke patients on unenhanced CT CT plus CT perfusion predicts stroke surgery outcomes CTA lowers costs, improves outcomes for minor stroke Can AI find brain hemorrhage as well as radiologists?
Source: AuntMinnie.com Headlines - February 11, 2020 Category: Radiology Source Type: news

Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks FUNCTIONAL
CONCLUSIONS: An unsupervised generative adversarial network can be used to obtain automated infarct lesion segmentations with a moderate Dice similarity coefficient and good volumetric correspondence.
Source: American Journal of Neuroradiology - August 8, 2022 Category: Radiology Authors: van Voorst, H., Konduri, P. R., van Poppel, L. M., van der Steen, W., van der Sluijs, P. M., Slot, E. M. H., Emmer, B. J., van Zwam, W. H., Roos, Y. B. W. E. M., Majoie, C. B. L. M., Zaharchuk, G., Caan, M. W. A., Marquering, H. A., on behalf of the CONTR Tags: FUNCTIONAL Source Type: research

A clinical –radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study
ConclusionThe proposed clinical –radiomics model is a dependable approach that could provide risk assessment of HT for patients who receive IVT after stroke.
Source: Insights into Imaging - March 29, 2023 Category: Radiology Source Type: research

Ultrasound imaging gauges muscle tightness after stroke
Ultrasound strain imaging can be an effective tool for assessing poststroke...Read more on AuntMinnie.comRelated Reading: MRI links lifestyle factors to stroke, dementia risk 5 risk factors help predict brain hemorrhage on CT AI algorithm can triage head CT exams for urgent review Ultrasound elastography helps identify invasive breast cancer AIUM: Can deep learning classify liver fibrosis on US?
Source: AuntMinnie.com Headlines - August 22, 2018 Category: Radiology Source Type: news

Can machine learning of post-procedural cone-beam CT images in acute ischemic stroke improve the detection of 24-h hemorrhagic transformation? A preliminary study
ConclusionML demonstrates high-sensitivity but low-accuracy 24-h HT prediction in AIS. The automated CB-CT imaging evaluation resizes sensitivity, specificity, and accuracy rates of visual interpretation reported in the literature so far. A standardized quantitative interpretation of CB-CT may be warranted to overcome the inter-operator variability.
Source: Neuroradiology - October 25, 2022 Category: Radiology Source Type: research

An East Coast Perspective on Artificial Intelligence and Machine Learning: Part 1
This article reviews the use of deep learning convolutional neural networks for managing hemorrhagic stroke. Such a capability may be used to alert appropriate care teams, make decisions about patient transport from a primary care center to a comprehensive stroke center, and assist in treatment selection. This article reviews artificial intelligence algorithms for intracranial hemorrhage detection, quantification, and prognostication. Multiple algorithms currently being explored are described and illustrated with the help of examples.
Source: Neuroimaging Clinics - September 16, 2020 Category: Radiology Authors: Rajiv Gupta, Sanjith Prahas Krishnam, Pamela W. Schaefer, Michael H. Lev, R. Gilberto Gonzalez Source Type: research

Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning
Graphical abstract: Highlights: ► Dictionaries are learned from high-dose CTP data for low-dose CBF estimation. ► Temporal convolution model is combined with spatial dictionary mapping prior. ► Evaluation on in vivo aneurysmal subarachnoid hemorrhage and normal patients. ► Outperform existing methods in CBF estimation for low-dose CTP data. ► Improve the differentiation between ischemic and normal tissues in the brain.Abstract: Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post...
Source: Medical Image Analysis - March 7, 2013 Category: Radiology Authors: Ruogu Fang, Tsuhan Chen, Pina C. Sanelli Source Type: research

Improving Sensitivity on Identification and Delineation of Intracranial Hemorrhage Lesion Using Cascaded Deep Learning Models
AbstractHighly accurate detection of the intracranial hemorrhage without delay is a critical clinical issue for the diagnostic decision and treatment in an emergency room. In the context of a study on diagnostic accuracy, there is a tradeoff between sensitivity and specificity. In order to improve sensitivity while preserving specificity, we propose a cascade deep learning model constructed using two convolutional neural networks (CNNs) and dual fully convolutional networks (FCNs). The cascade CNN model is built for identifying bleeding; hereafter the dual FCN is to detect five different subtypes of intracranial hemorrhage...
Source: Journal of Digital Imaging - January 24, 2019 Category: Radiology Source Type: research

Brain lesions on DWI-MRI linked to poor outcomes for some
Lesions that appear on diffusion-weighted imaging (DWI) MRI scans represent...Read more on AuntMinnie.comRelated Reading: Deep-learning model detects cerebral microbleeds on MRI Key MRI markers link vascular brain injury to health risks 5 risk factors help predict brain hemorrhage on CT Combination of ultrasound, tPA may boost stroke treatment CT guidance helps brain hemorrhage outcomes
Source: AuntMinnie.com Headlines - July 21, 2020 Category: Radiology Source Type: news

An East Coast Perspective on Artificial Intelligence and Machine Learning: Part 1: Hemorrhagic Stroke Imaging and Triage
Publication date: Available online 17 September 2020Source: Neuroimaging Clinics of North AmericaAuthor(s): Rajiv Gupta, Sanjith Prahas Krishnam, Pamela W. Schaefer, Michael H. Lev, R. Gilberto Gonzalez
Source: Neuroimaging Clinics of North America - September 18, 2020 Category: Radiology Source Type: research

Machine learning boosts chest CT's performance
Machine learning-based CT texture analysis software improves reader accuracy...Read more on AuntMinnie.comRelated Reading: AI can quantify hematoma in hemorrhagic stroke patients Large study confirms value of CT lung cancer screening CT radiation doses for COVID-19 patients vary widely CT lung screening scans also work for bone density CT lung screening program falls short in China
Source: AuntMinnie.com Headlines - November 18, 2020 Category: Radiology Source Type: news

CT radiomics helps predict spontaneous intracerebral hemorrhage
CT radiomics and a machine-learning model can predict hematoma expansion i...Read more on AuntMinnie.comRelated Reading: Portable MRI helps clarify stroke symptoms more quickly MIDRC offers support for COVID-19 AI AI detection of intracranial hemorrhage saves lives Study confirms viability of low-field MRI for ICH AI software bests Chinese physicians in contest
Source: AuntMinnie.com Headlines - October 18, 2021 Category: Radiology Source Type: news

Brain PET and Cerebrovascular Disease
Cerebrovascular disease encompasses a broad spectrum of diseases such as stroke, hemorrhage, and cognitive decline associated with vascular narrowing, obstruction, rupture, and inflammation, among other issues. Recent advances in hardware and software have led to improvements in brain PET. Although still in its infancy, machine learning using convolutional neural networks is gaining traction in this area, often with a focus on providing high-quality images with reduced noise using a shorter acquisition time or less radiation exposure for the patient.
Source: PET Clinics - October 27, 2022 Category: Radiology Authors: Katarina Chiam, Louis Lee, Phillip H. Kuo, Vincent C. Gaudet, Sandra E. Black, Katherine A. Zukotynski Source Type: research