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Total 224 results found since Jan 2013.

A Deep Learning-Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations ADULT BRAIN
CONCLUSIONS: Fast, automated deep learning–based image-quality rating can decrease rescan and recall rates, while rendering them technologist-independent. It was estimated that decreasing rescans and recalls from the technologists' values to the values of deep learning could save hospitals $24,000/scanner/year.
Source: American Journal of Neuroradiology - February 13, 2019 Category: Radiology Authors: Sreekumari, A., Shanbhag, D., Yeo, D., Foo, T., Pilitsis, J., Polzin, J., Patil, U., Coblentz, A., Kapadia, A., Khinda, J., Boutet, A., Port, J., Hancu, I. Tags: ADULT BRAIN 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

An East Coast Perspective on Artificial Intelligence and Machine Learning
This article reviews the use of deep learning convolutional neural networks in the management of ischemic stroke. Artificial intelligence-based algorithms may be used in patient triage to detect and sound the alarm based on early imaging, alert care teams, and assist in treatment selection. This article reviews algorithms for artificial intelligence techniques that may be used to detect and localize acute ischemic stroke. We describe artificial intelligence algorithms for these tasks and illustrate them with examples.
Source: Neuroimaging Clinics - October 8, 2020 Category: Radiology Authors: Rajiv Gupta, Sanjith Prahas Krishnam, Pamela W. Schaefer, Michael H. Lev, R. Gilberto Gonzalez Source Type: research

Deep-learning algorithm spots carotid calcium on CT exams
A deep-learning algorithm performs comparably to human readers when it come...Read more on AuntMinnie.comRelated Reading: Everyone wins when AI becomes seamless for radiologists RapidAI launches new stroke app Use of CTA, CT perfusion up -- but for the right reasons CT mobile stroke units improve patient outcomes Do angio before CT in acute stroke patients, Barcelona team urges
Source: AuntMinnie.com Headlines - July 1, 2021 Category: Radiology Source Type: news

QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping
CONCLUSION: QQ-NET improves the accuracy of QQ-based OEF with faster reconstruction.PMID:34719059 | DOI:10.1002/mrm.29057
Source: Magnetic Resonance in Medicine - October 31, 2021 Category: Radiology Authors: Junghun Cho Jinwei Zhang Pascal Spincemaille Hang Zhang Simon Hubertus Yan Wen Ramin Jafari Shun Zhang Thanh D Nguyen Alexey V Dimov Ajay Gupta Yi Wang Source Type: research

Development of a new body weight estimation method using head CT scout images
CONCLUSIONS: The presented new method is a potentially valuable support tool for medical staff, such as doctors and nurses, in estimating weight during emergency examinations for patients with acute conditions such as stroke when obtaining accurate weight measurements is not easily feasible.PMID:37545250 | DOI:10.3233/XST-230087
Source: Journal of X-Ray Science and Technology - August 7, 2023 Category: Radiology Authors: Tatsuya Kondo Manami Umezu Yohan Kondo Mitsuru Sato Tsutomu Kanazawa Yoshiyuki Noto Source Type: research