Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine
Purpose: Artificial intelligence (AI) has accelerated novel discoveries across multiple disciplines including medicine. Clinical medicine suffers from a lack of AI-based applications, potentially due to lack of awareness of AI methodology. Future collaboration between computer scientists and clinici...
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doaj-fd87be3a786c497b8cf657abad311beb2020-11-24T21:11:29ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2019-08-01910.3389/fonc.2019.00768475815Artificial Intelligence in the Management of Glioma: Era of Personalized MedicineHouman Sotoudeh0Omid Shafaat1Joshua D. Bernstock2Michael David Brooks3Galal A. Elsayed4Jason A. Chen5Paul Szerip6Gustavo Chagoya7Florian Gessler8Ehsan Sotoudeh9Amir Shafaat10Gregory K. Friedman11Department of Neuroradiology, University of Alabama, Birmingham, AL, United StatesRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United StatesDepartment of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United StatesDepartment of Neuroradiology, University of Alabama, Birmingham, AL, United StatesDepartment of Neurosurgery, University of Alabama, Birmingham, AL, United StatesMedical Scientist Training Program, University of California, Los Angeles, Los Angeles, CA, United StatesSenior Research Scientist, Uber AI Labs, San Francisco, CA, United StatesDepartment of Neurosurgery, University of Alabama, Birmingham, AL, United StatesDepartment of Neurosurgery, Goethe University, Frankfurt, GermanyDepartment of Surgery, Iranian Hospital, Dubai, United Arab EmiratesDepartment of Mechanical Engineering, Arak University of Technology, Arak, Iran0Division of Pediatric Hematology and Oncology, Department of Pediatrics, University of Alabama, Birmingham, AL, United StatesPurpose: Artificial intelligence (AI) has accelerated novel discoveries across multiple disciplines including medicine. Clinical medicine suffers from a lack of AI-based applications, potentially due to lack of awareness of AI methodology. Future collaboration between computer scientists and clinicians is critical to maximize the benefits of transformative technology in this field for patients. To illustrate, we describe AI-based advances in the diagnosis and management of gliomas, the most common primary central nervous system (CNS) malignancy.Methods: Presented is a succinct description of foundational concepts of AI approaches and their relevance to clinical medicine, geared toward clinicians without computer science backgrounds. We also review novel AI approaches in the diagnosis and management of glioma.Results: Novel AI approaches in gliomas have been developed to predict the grading and genomics from imaging, automate the diagnosis from histopathology, and provide insight into prognosis.Conclusion: Novel AI approaches offer acceptable performance in gliomas. Further investigation is necessary to improve the methodology and determine the full clinical utility of these novel approaches.https://www.frontiersin.org/article/10.3389/fonc.2019.00768/fullgliomaartificial intelligenceneural networkdeep neural networkconvolution neural networksupport vector machines |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Houman Sotoudeh Omid Shafaat Joshua D. Bernstock Michael David Brooks Galal A. Elsayed Jason A. Chen Paul Szerip Gustavo Chagoya Florian Gessler Ehsan Sotoudeh Amir Shafaat Gregory K. Friedman |
spellingShingle |
Houman Sotoudeh Omid Shafaat Joshua D. Bernstock Michael David Brooks Galal A. Elsayed Jason A. Chen Paul Szerip Gustavo Chagoya Florian Gessler Ehsan Sotoudeh Amir Shafaat Gregory K. Friedman Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine Frontiers in Oncology glioma artificial intelligence neural network deep neural network convolution neural network support vector machines |
author_facet |
Houman Sotoudeh Omid Shafaat Joshua D. Bernstock Michael David Brooks Galal A. Elsayed Jason A. Chen Paul Szerip Gustavo Chagoya Florian Gessler Ehsan Sotoudeh Amir Shafaat Gregory K. Friedman |
author_sort |
Houman Sotoudeh |
title |
Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine |
title_short |
Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine |
title_full |
Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine |
title_fullStr |
Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine |
title_full_unstemmed |
Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine |
title_sort |
artificial intelligence in the management of glioma: era of personalized medicine |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Oncology |
issn |
2234-943X |
publishDate |
2019-08-01 |
description |
Purpose: Artificial intelligence (AI) has accelerated novel discoveries across multiple disciplines including medicine. Clinical medicine suffers from a lack of AI-based applications, potentially due to lack of awareness of AI methodology. Future collaboration between computer scientists and clinicians is critical to maximize the benefits of transformative technology in this field for patients. To illustrate, we describe AI-based advances in the diagnosis and management of gliomas, the most common primary central nervous system (CNS) malignancy.Methods: Presented is a succinct description of foundational concepts of AI approaches and their relevance to clinical medicine, geared toward clinicians without computer science backgrounds. We also review novel AI approaches in the diagnosis and management of glioma.Results: Novel AI approaches in gliomas have been developed to predict the grading and genomics from imaging, automate the diagnosis from histopathology, and provide insight into prognosis.Conclusion: Novel AI approaches offer acceptable performance in gliomas. Further investigation is necessary to improve the methodology and determine the full clinical utility of these novel approaches. |
topic |
glioma artificial intelligence neural network deep neural network convolution neural network support vector machines |
url |
https://www.frontiersin.org/article/10.3389/fonc.2019.00768/full |
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