Literature DB >> 35765692

Neuroimaging in the Era of Artificial Intelligence: Current Applications.

Robert Monsour1, Mudit Dutta1, Ahmed-Zayn Mohamed1, Andrew Borkowski1,2, Narayan A Viswanadhan1,2.   

Abstract

Background: Artificial intelligence (AI) in medicine has shown significant promise, particularly in neuroimaging. AI increases efficiency and reduces errors, making it a valuable resource for physicians. With the increasing amount of data processing and image interpretation required, the ability to use AI to augment and aid the radiologist could improve the quality of patient care. Observations: AI can predict patient wait times, which may allow more efficient patient scheduling. Additionally, AI can save time for repeat magnetic resonance neuroimaging and reduce the time spent during imaging. AI has the ability to read computed tomography, magnetic resonance imaging, and positron emission tomography with reduced or without contrast without significant loss in sensitivity for detecting lesions. Neuroimaging does raise important ethical considerations and is subject to bias. It is vital that users understand the practical and ethical considerations of the technology. Conclusions: The demonstrated applications of AI in neuroimaging are numerous and varied, and it is reasonable to assume that its implementation will increase as the technology matures. AI's use for detecting neurologic conditions holds promise in combatting ever increasing imaging volumes and providing timely diagnoses.
Copyright © 2022 Frontline Medical Communications Inc., Parsippany, NJ, USA.

Entities:  

Year:  2022        PMID: 35765692      PMCID: PMC9227741          DOI: 10.12788/fp.0231

Source DB:  PubMed          Journal:  Fed Pract        ISSN: 1078-4497


  57 in total

1.  Applications of a deep learning method for anti-aliasing and super-resolution in MRI.

Authors:  Can Zhao; Muhan Shao; Aaron Carass; Hao Li; Blake E Dewey; Lotta M Ellingsen; Jonghye Woo; Michael A Guttman; Ari M Blitz; Maureen Stone; Peter A Calabresi; Henry Halperin; Jerry L Prince
Journal:  Magn Reson Imaging       Date:  2019-06-24       Impact factor: 2.546

Review 2.  Artificial intelligence for assisting diagnostics and assessment of Parkinson's disease-A review.

Authors:  Minja Belić; Vladislava Bobić; Milica Badža; Nikola Šolaja; Milica Đurić-Jovičić; Vladimir S Kostić
Journal:  Clin Neurol Neurosurg       Date:  2019-07-16       Impact factor: 1.876

3.  Thrombectomy 6 to 24 Hours after Stroke with a Mismatch between Deficit and Infarct.

Authors:  Raul G Nogueira; Ashutosh P Jadhav; Diogo C Haussen; Alain Bonafe; Ronald F Budzik; Parita Bhuva; Dileep R Yavagal; Marc Ribo; Christophe Cognard; Ricardo A Hanel; Cathy A Sila; Ameer E Hassan; Monica Millan; Elad I Levy; Peter Mitchell; Michael Chen; Joey D English; Qaisar A Shah; Frank L Silver; Vitor M Pereira; Brijesh P Mehta; Blaise W Baxter; Michael G Abraham; Pedro Cardona; Erol Veznedaroglu; Frank R Hellinger; Lei Feng; Jawad F Kirmani; Demetrius K Lopes; Brian T Jankowitz; Michael R Frankel; Vincent Costalat; Nirav A Vora; Albert J Yoo; Amer M Malik; Anthony J Furlan; Marta Rubiera; Amin Aghaebrahim; Jean-Marc Olivot; Wondwossen G Tekle; Ryan Shields; Todd Graves; Roger J Lewis; Wade S Smith; David S Liebeskind; Jeffrey L Saver; Tudor G Jovin
Journal:  N Engl J Med       Date:  2017-11-11       Impact factor: 91.245

Review 4.  Deep Learning: A Primer for Radiologists.

Authors:  Gabriel Chartrand; Phillip M Cheng; Eugene Vorontsov; Michal Drozdzal; Simon Turcotte; Christopher J Pal; Samuel Kadoury; An Tang
Journal:  Radiographics       Date:  2017 Nov-Dec       Impact factor: 5.333

5.  A Deep Learning-Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations.

Authors:  A Sreekumari; D Shanbhag; D Yeo; T Foo; J Pilitsis; J Polzin; U Patil; A Coblentz; A Kapadia; J Khinda; A Boutet; J Port; I Hancu
Journal:  AJNR Am J Neuroradiol       Date:  2019-01-03       Impact factor: 3.825

6.  Deep Generative Adversarial Neural Networks for Compressive Sensing MRI.

Authors:  Morteza Mardani; Enhao Gong; Joseph Y Cheng; Shreyas S Vasanawala; Greg Zaharchuk; Lei Xing; John M Pauly
Journal:  IEEE Trans Med Imaging       Date:  2018-07-23       Impact factor: 10.048

7.  Development of a prognostic model for mortality in COVID-19 infection using machine learning.

Authors:  Adam L Booth; Elizabeth Abels; Peter McCaffrey
Journal:  Mod Pathol       Date:  2020-10-16       Impact factor: 7.842

Review 8.  Radiomics, machine learning, and artificial intelligence-what the neuroradiologist needs to know.

Authors:  Matthias W Wagner; Khashayar Namdar; Asthik Biswas; Suranna Monah; Farzad Khalvati; Birgit B Ertl-Wagner
Journal:  Neuroradiology       Date:  2021-09-18       Impact factor: 2.804

Review 9.  Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging.

Authors:  Anke Meyer-Bäse; Lia Morra; Uwe Meyer-Bäse; Katja Pinker
Journal:  Contrast Media Mol Imaging       Date:  2020-08-28       Impact factor: 3.161

Review 10.  Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine.

Authors:  Filippo Pesapane; Marina Codari; Francesco Sardanelli
Journal:  Eur Radiol Exp       Date:  2018-10-24
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