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Deep Learning and Neurology: A Systematic Review
Copyright policy )Adis, Springer HealthcareDeep Learning and Neurology: A Systematic Review
Abstract Deciphering the massive volume of complex electronic data that has been compiled by hospital systems over the past decades has the potential to revolutionize modern medicine, as well as present significant challenges. Deep learning is uniquely suited to address these challenges, and recent advances in techniques and hardware have poised the field of medical machine learning for transformational growth. The clinical neurosciences are particularly well positioned to benefit from these advances given the subtle presentation of symptoms typical of neurologic disease. Here we review the various domains in which deep learning algorithms have already provided impetus for change—areas such as medical image analysis for the improved diagnosis of Alzheimer’s disease and the early detection of acute neurologic events; medical image segmentation for quantitative evaluation of neuroanatomy and vasculature; connectome mapping for the diagnosis of Alzheimer’s, autism spectrum disorder, and attention deficit hyperactivity disorder; and mining of microscopic electroencephalogram signals and granular genetic signatures. We additionally note important challenges in the integration of deep learning tools in the clinical setting and discuss the barriers to tackling the challenges that currently exist.
- Mount Sinai Health System United States
Library of Congress Subject Headings: lcsh:Neurology. Diseases of the nervous system lcsh:RC346-429
Microsoft Academic Graph classification: Connectome Cognitive science Medicine business.industry business Attention deficit hyperactivity disorder medicine.disease Health informatics Artificial intelligence Electronic data Deep learning Autism spectrum disorder Neurology medicine.medical_specialty Modern medicine
Artificial intelligence, Biomedical informatics, Computer vision, Connectome mapping, Deep learning, Genomics, Review, Machine learning, Neurology, Neuroscience, Neurology (clinical)
Artificial intelligence, Biomedical informatics, Computer vision, Connectome mapping, Deep learning, Genomics, Review, Machine learning, Neurology, Neuroscience, Neurology (clinical)
Library of Congress Subject Headings: lcsh:Neurology. Diseases of the nervous system lcsh:RC346-429
Microsoft Academic Graph classification: Connectome Cognitive science Medicine business.industry business Attention deficit hyperactivity disorder medicine.disease Health informatics Artificial intelligence Electronic data Deep learning Autism spectrum disorder Neurology medicine.medical_specialty Modern medicine
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Jensen, PB, Jensen, LJ, Brunak, S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012; 13 (6): 395-405 [PubMed]
Luo, J, Wu, M, Gopukumar, D, Zhao, Y. Big data application in biomedical research and health care: a literature review. Biomed Inform Insights. 2016; 19 (8): 1-10 [OpenAIRE]
Kohli, MD, Summers, RM, Geis, JR. Medical image data and datasets in the era of machine learning-whitepaper from the 2016 C-MIMI meeting dataset session. J Digit Imaging. 2017; 30 (4): 392-399 [OpenAIRE] [PubMed]
Bengio, Y, Courville, A, Vincent, P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013; 35 (8): 1798-1828 [OpenAIRE] [PubMed]
LeCun, Y, Bengio, Y, Hinton, G. Deep learning. Nature. 2015; 521 (7553): 436-444 [OpenAIRE] [PubMed]
6.Li H, Lin Z, Shen X, Brandt J, Hua G. A convolutional neural network cascade for face detection. In: Proceedings of IEEE conference on computer vision and pattern recognition. Boston, MA. 2015. pp. 5325–34.
7.Gilani SZ, Mian A. Learning from millions of 3D scans for large-scale 3D face recognition. 2017. http://arxiv.org/abs/1711.05942.
8.Ramanishka V, Chen Y-T, Misu T, Saenko K. Toward driving scene understanding: a dataset for learning driver behavior and causal reasoning. In: Proceedings of IEEE conference on computer vision and pattern recognition. Salt Lake City, UT. 2018. pp. 7699–707.
9.Maqueda AI, Loquercio A, Gallego G, Garcia N, Scaramuzza D. Event-based vision meets deep learning on steering prediction for self-driving cars. 2018. http://arxiv.org/abs/1804.01310.
10.Mazaré P-E, Humeau S, Raison M, Bordes A. Training millions of personalized dialogue agents. 2018. http://arxiv.org/abs/180 9.01984.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).60 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Substantial influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Substantial citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).60 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Substantial influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Substantial
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- Mount Sinai Health System United States
Abstract Deciphering the massive volume of complex electronic data that has been compiled by hospital systems over the past decades has the potential to revolutionize modern medicine, as well as present significant challenges. Deep learning is uniquely suited to address these challenges, and recent advances in techniques and hardware have poised the field of medical machine learning for transformational growth. The clinical neurosciences are particularly well positioned to benefit from these advances given the subtle presentation of symptoms typical of neurologic disease. Here we review the various domains in which deep learning algorithms have already provided impetus for change—areas such as medical image analysis for the improved diagnosis of Alzheimer’s disease and the early detection of acute neurologic events; medical image segmentation for quantitative evaluation of neuroanatomy and vasculature; connectome mapping for the diagnosis of Alzheimer’s, autism spectrum disorder, and attention deficit hyperactivity disorder; and mining of microscopic electroencephalogram signals and granular genetic signatures. We additionally note important challenges in the integration of deep learning tools in the clinical setting and discuss the barriers to tackling the challenges that currently exist.