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Specialty: Radiology
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Total 224 results found since Jan 2013.

Developing new quantitative CT image markers to predict prognosis of acute ischemic stroke patients
CONCLUSIONS: This study demonstrates feasibility of developing a new quantitative imaging method and marker to predict AIS patients' prognosis in the hyperacute stage, which can help clinicians optimally treat and manage AIS patients.PMID:35213340 | DOI:10.3233/XST-221138
Source: Journal of X-Ray Science and Technology - February 25, 2022 Category: Radiology Authors: Gopichandh Danala Bappaditya Ray Masoom Desai Morteza Heidari Seyedehnafiseh Mirniaharikandehei Sai Kiran R Maryada Bin Zheng Source Type: research

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

Validation of a machine learning software tool for automated large vessel occlusion detection in patients with suspected acute stroke
ConclusionThe StrokeSENS LVO machine learning algorithm detects anterior LVO with high accuracy from a range of scans in a large dataset.
Source: Neuroradiology - November 9, 2022 Category: Radiology Source Type: research

Machine-learning algorithm in acute stroke: real-world experience
To assess the clinical performance of a commercially available machine learning (ML) algorithm in acute stroke.
Source: Clinical Radiology - November 18, 2022 Category: Radiology Authors: N. Chan, N. Sibtain, T. Booth, P. de Souza, S. Bibby, Y.-H. Mah, J. Teo, J.M. U-King-Im Source Type: research

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