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      Artificial Intelligence Applications in the Imaging of Epilepsy and Its Comorbidities: Present and Future

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          Abstract

          Artificial intelligence (AI) is increasingly used in medical image analysis and has accelerated scientific discoveries across fields of medicine. In this review, we highlight how AI has been applied to neuroimaging in patients with epilepsy to enhance classification of clinical diagnosis, prediction of treatment outcomes, and the understanding of cognitive comorbidities. We outline the strengths and shortcomings of current AI research and the need for future studies using large datasets that test the reproducibility and generalizability of current findings, as well as studies that test the clinical utility of AI approaches.

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          Most cited references47

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          Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

          Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
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            Big Data and Machine Learning in Health Care

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              Points of Significance: Statistics versus machine learning

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                Author and article information

                Journal
                Epilepsy Curr
                Epilepsy Curr
                spepi
                EPI
                Epilepsy Currents
                SAGE Publications (Sage CA: Los Angeles, CA )
                1535-7597
                1535-7511
                12 January 2022
                Mar-Apr 2022
                : 22
                : 2
                : 91-96
                Affiliations
                [1 ]Department of Neurology, Ringgold 28132, universityUniversity of Campinas; , Campinas, Brazil
                [2 ]Department of Psychiatry, Center for Multimodal Imaging and Genetics (CMIG), Ringgold 8784, universityUniversity of California; , San Diego, CA, USA
                Author notes
                [*] [* ]Carrie R. McDonald, PhD, Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92093, USA. Email: camcdonald@ 123456health.ucsd.edu
                Author information
                https://orcid.org/0000-0001-9336-9568
                https://orcid.org/0000-0002-0721-5640
                Article
                10.1177_15357597211068600
                10.1177/15357597211068600
                8988724
                35444507
                eb65e36b-d4ae-44b7-987f-780af62bfaad
                © The Author(s) 2022

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                Funding
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico, FundRef https://doi.org/10.13039/501100003593;
                Award ID: 465346/2014-6
                Award ID: 311231/2019-5
                Funded by: National Institute of Neurological Disorders and Stroke, FundRef https://doi.org/10.13039/100000065;
                Award ID: R01NS120976
                Award ID: R01NS122827
                Award ID: R01NS065838
                Award ID: R21NS10773
                Funded by: Sao Paulo Research Foundation;
                Award ID: 2013/07559-3
                Categories
                Current Review in Clinical Research
                Custom metadata
                March-April 2022
                ts10

                magnetic resonance imaging,functional imaging,structural imaging,machine learning,deep learning,cognition,seizures

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