Literature DB >> 33039000

Diverse Applications of Artificial Intelligence in Neuroradiology.

Michael Tran Duong1, Andreas M Rauschecker2, Suyash Mohan3.   

Abstract

Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of AI, including workflow optimization, lesion segmentation, and precision education. Given the many modalities used in diagnosing neurologic diseases, AI may be deployed to integrate across modalities (MR imaging, computed tomography, PET, electroencephalography, clinical and laboratory findings), facilitate crosstalk among specialists, and potentially improve diagnosis in patients with trauma, multiple sclerosis, epilepsy, and neurodegeneration. Together, there are myriad applications of AI for neuroradiology."
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Deep learning; Epilepsy; Multiple sclerosis; Neural network; Neurodegeneration; Neuroradiology; Trauma

Year:  2020        PMID: 33039000     DOI: 10.1016/j.nic.2020.07.003

Source DB:  PubMed          Journal:  Neuroimaging Clin N Am        ISSN: 1052-5149            Impact factor:   2.264


  2 in total

1.  Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging.

Authors:  Manoj Mannil; Nicolin Hainc; Risto Grkovski; Sebastian Winklhofer
Journal:  Acta Neurochir Suppl       Date:  2022

Review 2.  Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis.

Authors:  Wilson Ong; Lei Zhu; Wenqiao Zhang; Tricia Kuah; Desmond Shi Wei Lim; Xi Zhen Low; Yee Liang Thian; Ee Chin Teo; Jiong Hao Tan; Naresh Kumar; Balamurugan A Vellayappan; Beng Chin Ooi; Swee Tian Quek; Andrew Makmur; James Thomas Patrick Decourcy Hallinan
Journal:  Cancers (Basel)       Date:  2022-08-20       Impact factor: 6.575

  2 in total

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