Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Neurology and Therapy
Published in: Neurology and Therapy 2/2019

Open Access 01-12-2019 | Attention Deficit Hyperactivity Disorder | Review

Deep Learning and Neurology: A Systematic Review

Authors: Aly Al-Amyn Valliani, Daniel Ranti, Eric Karl Oermann

Published in: Neurology and Therapy | Issue 2/2019

Login to get access

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.
Literature
1.
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.PubMedCrossRef
2.
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.
3.
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):392–9.PubMedPubMedCentralCrossRef
4.
Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35(8):1798–828.PubMedCrossRef
5.
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.
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.
10.
11.
12.
13.
14.
Mitchell TM. The discipline of machine learning, vol. 9. Pittsburgh: School of Computer Science, Carnegie Mellon University; 2006.
15.
16.
Ogutu JO, Schulz-Streeck T, Piepho H-P. Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proc. 2012;6[Suppl 2]:S10.PubMedPubMedCentralCrossRef
17.
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ, editors. Advances in neural information processing systems, vol. 25. New York: Curran Associates, Inc.; 2012; 1097–105.
18.
19.
Saba L, Biswas M, Kuppili V, et al. The present and future of deep learning in radiology. Eur J Radiol. 2019;114:14–24.PubMedCrossRef
20.
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10.PubMedCrossRef
21.
22.
Haenssle HA, Fink C, Schneiderbauer R, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29(8):1836–42.PubMedCrossRef
23.
De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342–50.PubMedCrossRef
24.
Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2(3):158–64.PubMedCrossRef
25.
26.
Rumelhart DE, McClelland JL. Learning internal representations by error propagation. In: Parallel distributed processing: explorations in the microstructure of cognition: foundations. Wachtendonk: MITP Verlags-GmbH & Co. KG; 1987. pp. 318–62.
27.
Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006;313(5786):504–7.PubMedCrossRef
28.
29.
Shin H-C, Tenenholtz NA, Rogers JK, et al. Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In:Proc Third International Workshop, SASHIMI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018. In: Gooya A, Goksel O, Oguz I, Burgos N, editors. Simulation and synthesis in medical imaging. Cham: Springer International Publishing; 2018:1–11.
30.
31.
32.
Petersen RC, Aisen PS, Beckett LA, et al. Alzheimer’s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology. 2010;74(3):201–9.PubMedPubMedCentralCrossRef
33.
Menze BH, Jakab A, Bauer S, et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging. 2015;34(10):1993–2024.PubMedCrossRef
34.
Suk H-I, Shen D. Deep learning-based feature representation for AD/MCI classification. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):583–90.PubMedPubMedCentral
35.
Gupta A, Ayhan M, Maida A. Natural image bases to represent neuroimaging data. In: Proceedings of 30th international conference on machine learning. vol. 28. Atlanta, GA. 2013. pp. 987–94.
36.
Li F, Tran L, Thung K-H, Ji S, Shen D, Li J. Robust deep learning for improved classification of AD/MCI Patients. Machine learning in medical imaging. New York: Springer International Publishing; 2014:240–7.CrossRef
37.
Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D. Early diagnosis of Alzheimer’s disease with deep learning. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI). Beijing, China. 2014. pp. 1015–8. http://​ieeexplore.​ieee.​org.
38.
Liu S, Liu S, Cai W, et al. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans Biomed Eng. 2015;62(4):1132–40.PubMedCrossRef
39.
Suk H-I, Lee S-W, Shen D. Alzheimer’s disease neuroimaging initiative. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct Funct. 2015;220(2):841–59.PubMedCrossRef
40.
41.
Suk H-I, Lee S-W, Shen D. Alzheimer’s disease neuroimaging initiative. Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis. Brain Struct Funct. 2016;221(5):2569–87.PubMedCrossRef
42.
Valliani A, Soni A. Deep residual nets for improved Alzheimer’s diagnosis. In: BCB. Boston, MA. 2017. p. 615.
43.
44.
45.
Hosseini-Asl E, Ghazal M, Mahmoud A, et al. Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network. Front Biosci. 2018;1(23):584–96.
46.
47.
Ding Y, Sohn JH, Kawczynski MG, et al. A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology. 2019;290(2):456–64.PubMedCrossRef
48.
Titano JJ, Badgeley M, Schefflein J, et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med. 2018;24(9):1337–41.PubMedCrossRef
49.
Zech J, Pain M, Titano J, et al. Natural language-based machine learning models for the annotation of clinical radiology reports. Radiology. 2018;30:171093.
50.
Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ,et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit Med. 2018;1(1):9.PubMedPubMedCentralCrossRef
51.
Chilamkurthy S, Ghosh R, Tanamala S, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 2018;392(10162):2388–96.PubMedCrossRef
52.
53.
Wachinger C, Reuter M, Klein T. DeepNAT: deep convolutional neural network for segmenting neuroanatomy. Neuroimage. 2018;15(170):434–45.CrossRef
54.
Ohgaki H, Kleihues P. Population-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas. J Neuropathol Exp Neurol. 2005;64(6):479–89.PubMedCrossRef
55.
56.
Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33(3):341–55.PubMedCrossRef
57.
Landman B, Warfield S. MICCAI 2012 workshop on multi-atlas labeling. In: Medical image computing and computer assisted intervention conference. Nice, France. October 1–5, 2012.
58.
Livne M, Rieger J, Aydin OU, et al. A U-Net deep learning framework for high performance vessel segmentation in patients with cerebrovascular disease. Front Neurosci. 2019;28(13):97.CrossRef
59.
Loftis JM, Huckans M, Morasco BJ. Neuroimmune mechanisms of cytokine-induced depression: current theories and novel treatment strategies. Neurobiol Dis. 2010;37(3):519–33.PubMedCrossRef
60.
61.
Lian C, Zhang J, Liu M, et al. Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images. Med Image Anal. 2018;46:106–17.PubMedPubMedCentralCrossRef
62.
Jeong Y, Rachmadi MF, Valdés-Hernández MDC, Komura T. Dilated saliency U-Net for white matter hyperintensities segmentation using irregularity age map. Front Aging Neurosci. 2019;27(11):150.CrossRef
63.
Gootjes L, Teipel SJ, Zebuhr Y, et al. Regional distribution of white matter hyperintensities in vascular dementia, Alzheimer’s disease and healthy aging. Dement Geriatr Cogn Disord. 2004;18(2):180–8.PubMedCrossRef
64.
Karargyros A, Syeda-Mahmood T. Saliency U-Net: A regional saliency map-driven hybrid deep learning network for anomaly segmentation. In: Medical imaging 2018: computer-aided diagnosis. International Society for Optics and Photonics. Houston, TX. 2018. 105751T.
65.
66.
Suk H-I, Wee C-Y, Lee S-W, Shen D. State-space model with deep learning for functional dynamics estimation in resting-state fMRI. Neuroimage. 2016;1(129):292–307.CrossRef
67.
Meszlényi RJ, Buza K, Vidnyánszky Z. Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture. Front Neuroinform. 2017;17(11):61.CrossRef
68.
Montufar GF, Pascanu R, Cho K, Bengio Y. On the number of linear regions of deep neural networks. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, editors. Advances in neural information processing systems, vol. 27. Red Hook: Curran Associates, Inc.; 2014:2924–32.
69.
Iidaka T. Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex. 2015;63:55–67.PubMedCrossRef
70.
Chen H, Duan X, Liu F, et al. Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity—a multi-center study. Prog Neuropsychopharmacol Biol Psychiatry. 2016;4(64):1–9.CrossRef
71.
Kuang D, Guo X, An X, Zhao Y, He L. Discrimination of ADHD based on fMRI data with deep belief network. Intelligent computing in bioinformatics. New York: Springer International Publishing; 2014:225–32.CrossRef
72.
Tjepkema-Cloostermans MC, de Carvalho RCV, van Putten MJAM. Deep learning for detection of focal epileptiform discharges from scalp EEG recordings. Clin Neurophysiol. 2018;129(10):2191–6.PubMedCrossRef
73.
Tsiouris ΚΜ, Pezoulas VC, Zervakis M, Konitsiotis S, Koutsouris DD, Fotiadis DI. A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals. Comput Biol Med. 2018;1(99):24–37.CrossRef
74.
Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med. 2018;1(100):270–8.CrossRef
75.
Truong ND, Nguyen AD, Kuhlmann L, Bonyadi MR, Yang J, Kavehei O. A generalised seizure prediction with convolutional neural networks for intracranial and scalp electroencephalogram data analysis. 2017. http://​arxiv.​org/​abs/​1707.​01976.
76.
Khan H, Marcuse L, Fields M, Swann K, Yener B. Focal onset seizure prediction using convolutional networks. IEEE Trans Biomed Eng. 2018;65(9):2109–18.PubMedCrossRef
77.
Yousefi S, Amrollahi F, Amgad M, et al. Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Sci Rep. 2017;7(1):11707.PubMedPubMedCentralCrossRef
78.
Zhou J, Park CY, Theesfeld CL, et al. Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nat Genet. 2019;51(6):973–80.PubMedPubMedCentralCrossRef
79.
Buda M, Saha A, Mazurowski MA. Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. Comput Biol Med. 2019;109:218–25.PubMedCrossRef
80.
Mobadersany P, Yousefi S, Amgad M, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci USA. 2018;115(13):E2970–9.PubMedPubMedCentralCrossRef
81.
Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. 2018;15(11):e1002683.PubMedPubMedCentralCrossRef
82.
83.
Obermeyer Z, Mullainathan S. Dissecting racial bias in an algorithm that guides health decisions for 70 million people. In: Proceedings of conference on fairness, accountability, and transparency. New York: ACM; 2019. p. 89.CrossRef
Metadata
Title
Deep Learning and Neurology: A Systematic Review
Authors
Aly Al-Amyn Valliani
Daniel Ranti
Eric Karl Oermann
Publication date
01-12-2019
Publisher
Springer Healthcare
Published in
Neurology and Therapy / Issue 2/2019
Print ISSN: 2193-8253
Electronic ISSN: 2193-6536
DOI
https://doi.org/10.1007/s40120-019-00153-8

Other articles of this Issue 2/2019

Neurology and Therapy 2/2019 Go to the issue

Review

Burden of Disease and Current Management of Dementia with Lewy Bodies: A Literature Review

Commentary

Two Sides to Every Story: Perspectives from Four Patients and a Healthcare Professional on Multiple Sclerosis Disease Progression

Correction

Correction to: Abstracts from the 5th International Porto Congress of Multiple Sclerosis

Original Research

A Novel Dual-Frequency Deep Brain Stimulation Paradigm for Parkinson’s Disease

Original Research

Effect of Anodal tDCS on Articulatory Accuracy, Word Production, and Syllable Repetition in Subjects with Aphasia: A Crossover, Double-Blinded, Sham-Controlled Trial

Acknowledgement

Acknowledgement to Authors, Referees and Readers 2019