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Total 224 results found since Jan 2013.

Ischemic stroke subtyping method combining convolutional neural network and radiomics
CONCLUSION: The experimental results show that the proposed method can effectively predict the subtype of IS and has potential to assist doctors in making timely and accurate diagnoses of IS patients.PMID:36591693 | DOI:10.3233/XST-221284
Source: Journal of X-Ray Science and Technology - January 2, 2023 Category: Radiology Authors: Yang Chen Yiwen He Zhuoyun Jiang Yuanzhong Xie Shengdong Nie Source Type: research

A new method for predicting the prognosis of ischemic stroke based vascular structure features and lesion location features
Determining the changes in the prognosis of the cerebral infarction area has an important guiding role in the selection of the treatment plan. The goal of this study is to propose a machine learning-based method that can predict the prognosis of stroke effectively and efficiently.
Source: Clinical Imaging - March 14, 2023 Category: Radiology Authors: Suiqing Weng, Xilin Sun, Hao Wang, Bin Song, Jie Zhu Tags: Neuroradiology Source Type: research

Identifying ex vivo acute ischemic stroke thrombus composition using electrochemical impedance spectroscopy
CONCLUSION: Combination of EIS and machine learning can reliably predict the RBC composition of retrieved ex vivo AIS thrombi and then classify them into groups according to their RBC composition with good sensitivity and specificity.PMID:37192738 | DOI:10.1177/15910199231175377
Source: Interventional Neuroradiology - May 16, 2023 Category: Radiology Authors: Jean Darcourt Waleed Brinjikji Olivier Fran çois Alice Giraud Collin R Johnson Smita Patil Senna Staessens Ramanathan Kadirvel Mahmoud H Mohammaden Leonardo Pisani Gabriel Martins Rodrigues Nicole M Cancelliere Vitor Mendes Pereira Franz Bozsak Karen Doy Source Type: research

Outcomes of middle cerebral artery angioplasty and stenting with Wingspan at a high-volume center
Conclusion Intracranial stenting of MCA stenoses may have the potential of better clinical outcomes if patients are properly selected and treated by an experienced operator at a high-volume center.
Source: Neuroradiology - October 29, 2015 Category: Radiology Source Type: research

Women less likely to receive poststroke imaging
Women who have experienced ischemic stroke are less likely than men to be evaluated...Read more on AuntMinnie.comRelated Reading: Start-up BURL develops ultrasound-based 'ECG for stroke' Machine learning can predict stroke treatment outcomes Medical groups urge endovascular training for stroke 3 CTA signs show which stroke patients can skip surgery Northwestern Medicine hastens stroke care with mobile CT unit
Source: AuntMinnie.com Headlines - January 31, 2019 Category: Radiology Source Type: news

Automated quantification of cerebral edema following hemispheric infarction: Application of a machine-learning algorithm to evaluate CSF shifts on serial head CTs
In conclusion, we have developed and validated an accurate automated approach to segment CSF and calculate its shifts on serial CT scans. This algorithm will allow us to efficiently and accurately measure the evolution of cerebral edema in future studies including large multi-site patient populations.
Source: NeuroImage: Clinical - September 25, 2016 Category: Radiology Source Type: research

Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning.
CONCLUSIONS: Estimates of NODDI and GFA parameters estimated simultaneously with a deep learning network from highly undersampled q-space data were improved compared to other state-of-the-art methods providing a 10-fold reduction scan time compared to conventional methods. PMID: 30426558 [PubMed - as supplied by publisher]
Source: Magnetic Resonance in Medicine - November 13, 2018 Category: Radiology Authors: Gibbons EK, Hodgson KK, Chaudhari AS, Richards LG, Majersik JJ, Adluru G, DiBella EVR Tags: Magn Reson Med Source Type: research

Simultaneous NODDI and GFA parameter map generation from subsampled q ‐space imaging using deep learning
ConclusionsEstimates of NODDI and GFA parameters estimated simultaneously with a deep learning network from highly undersampled q ‐space data were improved compared to other state‐of‐the‐art methods providing a 10‐fold reduction scan time compared to conventional methods.
Source: Magnetic Resonance in Medicine - November 13, 2018 Category: Radiology Authors: Eric K. Gibbons, Kyler K. Hodgson, Akshay S. Chaudhari, Lorie G. Richards, Jennifer J. Majersik, Ganesh Adluru, Edward V.R. DiBella Tags: FULL PAPER Source Type: research

Multivariate prediction of functional outcome using lesion topography characterized by acute diffusion tensor imaging
Publication date: Available online 10 April 2019Source: NeuroImage: ClinicalAuthor(s): Eric Moulton, Romain Valabregue, Stephane Lehéricy, Yves Samson, Charlotte RossoAbstractThe relationship between stroke topography and functional outcome has largely been studied with binary manual lesion segmentations. However, stroke topography may be better characterized by continuous variables capable of reflecting the severity of ischemia, which may be more pertinent for long-term outcome. Diffusion Tensor Imaging (DTI) constitutes a powerful means of quantifying the degree of acute ischemia and its potential relation to functional...
Source: NeuroImage: Clinical - April 11, 2019 Category: Radiology Source Type: research

AI algorithm can detect, quantify brain infarcts
Researchers discussed how they used a deep-learning algorithm to detect, quantify,...Read more on AuntMinnie.comRelated Reading: CTA spots COVID-19 in stroke patients Algorithm detects LVO stroke on multiphase CTA What will be the killer apps for AI in MRI? AI predicts final infarct lesion for stroke on MRI AI may help improve management of stroke patients
Source: AuntMinnie.com Headlines - November 29, 2020 Category: Radiology Source Type: news

Prediction of incident cardiovascular events using machine learning and CMR radiomics
ConclusionsRadiomics features may provide incremental predictive value over VRF and CMR indices in the prediction of incident CVDs.Key Points•Prediction of incident atrial fibrillation, heart failure, stroke, and myocardial infarction using machine learning techniques.•CMR radiomics, vascular risk factors, and standard CMR indices will be considered in the machine learning models.•The experiments show that radiomics features can provide incremental predictive value over VRF and CMR indices in the prediction of incident cardiovascular diseases.
Source: European Radiology - December 13, 2022 Category: Radiology Source Type: research

Automated estimation of ischemic core volume on noncontrast-enhanced CT via machine learning
CONCLUSION: The ischemic core volumes calculated by the described ML approach on NCCT approximate those obtained by MRI in patients with AIS who present beyond 1 hour from stroke onset.PMID:36572984 | DOI:10.1177/15910199221145487
Source: Interventional Neuroradiology - December 27, 2022 Category: Radiology Authors: Iris E Chen Brian Tsui Haoyue Zhang Joe X Qiao William Hsu May Nour Noriko Salamon Luke Ledbetter Jennifer Polson Corey Arnold Mersedeh BahrHossieni Reza Jahan Gary Duckwiler Jeffrey Saver David Liebeskind Kambiz Nael Source Type: research