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Neurology, Neuropsychiatry, Psychosomatics

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Digital technologies in the diagnosis and treatment of neurological diseases

https://doi.org/10.14412/2074-2711-2019-4-104-110

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Abstract

The review considers works devoted to convolutional neural networks as a main method for digital image processing, as well as to the diagnosis of neurological diseases based on computer-aided analysis of magnetic resonance imaging and electroencephalography. It describes approaches to building computer-aided diagnostic systems and gives examples of these systems in neurology. The virtual reality technology used to rehabilitate patients with imbalance, posttraumatic disorders, and consequences of stroke is presented. Digitalization is stated to be one of the priority areas for medicine development.

About the Authors

N. V. Petukhova
V.A. Trapeznikov Institute of Management Problems, Russian Academy of Sciences
Russian Federation
65, Profsoyuznaya St, Build. 1, Moscow 117997


M. P. Farkhadov
V.A. Trapeznikov Institute of Management Problems, Russian Academy of Sciences
Russian Federation
65, Profsoyuznaya St, Build. 1, Moscow 117997


M. V. Zamegrad
Department of Neurology, Russian Medical Academy of Continuing Professional Education, Ministry of Health of Russia; Russian Research and Clinical Center of Gerontology (Separate Subdivision), N.I. Pirogov Russian National Research Medical University, Ministry of Health of Russia
Russian Federation

2/1, Barrikadnaya St., Build. 1, Moscow 125993;

16, First Leonov St., Moscow 129226



S. P. Grachev
Department of Cardiology, A.I. Evdokimov Moscow State University of Medicine and Dentistry, Ministry of Health of Russia
Russian Federation
11/1, Yauzskaya St., Moscow 109240


References

1. Koikkalainen J, Rhodius-Meester H, Tolonen A, et al. Differential diagnosis of neurodegenerative diseases using structural MRI data. Neuroimage Clin. 2016 Mar 5;11: 435-449. doi: 10.1016/j.nicl.2016.02.019. eCollection 2016.

2. Tolonen A, Rhodius-Meester HFM, Bruun M, et al. Data-Driven Differential Diagnosis of Dementia Using Multiclass Disease State Index Classifier. Front Aging Neurosci. 2018 Apr 25;10: 111. doi: 10.3389/fnagi.2018.00111. eCollection 2018.

3. Wang SH, Phillips P, Sui Y, et al. Classification of Alzheimer's Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling. J Med Syst. 2018 Mar 26;42(5):85. doi: 10.1007/s10916-018-0932-7.

4. Wong SC, Gatt A, Stamatescu V, McDonnell MD. Understanding data augmentation for classification: when to warp? In International Conference on Digital Image Computing: Techniques and Applications (DICTA). Gold Coast: QLD; 2016. P. 1-6.

5. Bilello M, Arkuszewski M, Nucifora P, et al. Multiple sclerosis: identification of temporal changes in brain lesions with computer-assisted detection software. Neuroradiol J. 2013 Apr;26(2): 143-50. Epub 2013 May 10.

6. Wang S, Tang C, Sun J, et al. Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling. Front Neurosci. 2018 Nov 8;12:818.doi: 10.3389/fnins.2018.00818. eCollection 2018.

7. Zhang YD, Pan C, Sun J, Tang C. Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU. J Comput Sci. 2018;28:1-10. doi: 10.1016/j.jocs.2018.07.003.

8. Liu A, Hahn JS, Heldt GP, Coen R. Detection of neonatal seizures through computerized EEG analysis. Electroencephalogr Clin Neurophysiol. 1992 Jan;82(1):30-7.

9. Altunay S, Telatar Z, Erogul O. Epileptic EEG detection using the linear prediction error energy. Expert Syst Appl. 2010;37(8):5661-5665.

10. Tzallas AT, Tsipouras MG, Fotiadis DI. Epileptic seizure detection in EEGs using timefrequency analysis. IEEE Trans Inf Technol Biomed. 2009 Sep;13(5):703-10. doi: 10.1109/TITB.2009.2017939. Epub 2009 Mar 16.

11. Mormann F, Kreuz T, Rieke C, et al. On the predictability of epileptic seizures. Clin Neurophysiol. 2005 Mar;116(3):569-87. Epub 2005 Jan 6.

12. Zandi AS, Dumont GA, Javidan M, Tafreshi R. An entropy-based approach to predict seizures in temporal lobe epilepsy using scalp EEG. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:228-31. doi: 10.1109/IEMBS.2009.5333971.

13. Giannakakis G, Sakkalis V, Pediaditis M, Tsiknakis M. Methods for Seizure Detection and Prediction: An Overview Modern Electroencephalographic Assessment Techniques. Neuromethods. 2014;91:131-157,

14. Ulate-Campos A, Coughlin F, Gainza-Lein M, et al. Automated seizure detection systems and their effectiveness for each type of seizure. Seizure. 2016 Aug;40: 88-101. doi: 10.1016/j.seizure.2016.06.008. Epub 2016 Jun 17.

15. Cook MJ, O'Brien TJ, Berkovic SF, et al. Prediction of seizure likelihood with a longterm, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-inman study. Lancet Neurol. 2013 Jun;12(6):563-71. doi: 10.1016/S1474-4422(13)70075-9. Epub 2013 May 2.

16. Stacey WC. Seizure Prediction Is PossibleNow Let's Make It Practical. EBioMedicine. 2018 Jan;27:3-4. doi: 10.1016/j.ebiom.2018.01.006. Epub 2018 Jan 5.

17. Kiral-Kornek I, Roy S, Nurse E, et al. Epileptic seizure prediction using big data and deep learning: toward a mobile system. EBioMedicine. 2018 Jan;27:103-111. doi: 10.1016/j.ebiom.2017.11.032. Epub 2017 Dec 12.

18. Joseёok M, Krahn T, Sauer J. A Survey on Expert Systems for Diagnosis Support in the Field of Neurology. In: Howlett RJ, Jain LC, editors. Intelligent Decision Technologies – Proceedings of the 4th International Conference on Intelligent Decision Technologies (IDT'2015). Springer; 2015. P. 1-10.

19. Bilgi NB, Wali GM, Mense A, et al. Symptomatic Decision Support System for Neurological Disorders. Brain. 2017;8(4):5-16. https://www.edusoft.ro/brain/index.php/brain/article/viewFile/722/806.

20. Vandewiele G. Enhancing white-box machine learning processes by incorporating semantic background knowledge. The Extended Semantic Web Conference; 2017. P. 267-278. https://biblio.ugent.be/publication/8537055/file/8537057.pdf

21. Rammazzo L, Kikidis D, Anwer A, et al. EMBalance - validation of a decision support system in the early diagnostic evaluation and management plan formulation of balance disorders in primary care: study protocol of a feasibility randomised controlled trial. Trials. 2016 Sep 5;17(1):435. doi: 10.1186/s13063-016-1568-x.

22. Kondziolka D, Cooper BT, Lunsford LD, Silverman J. Development, implementation, and use of a local and global clinical registry for neurosurgery. Big Data. 2015 Jun;3(2):80-9. doi: 10.1089/big.2014.0069.

23. Rodriguez-de-Pablo C, Perry JC, Cavallaro FI, et al. Development of computer games for assessment and training in poststroke arm telerehabilitation. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:4571-4. doi: 10.1109/EMBC.2012.6346984.

24. Gal N, Andrei D, Nemes DI, et al. A Kinect based intelligent e-rehabilitation system in physical therapy. Stud Health Technol Inform. 2015;210:489-93.

25. Hoda M, Hoda Y, Alamri A, et al. A Novel Study on Natural Robotic Rehabilitation Exergames Using the Unaffected Arm of Stroke Patients. International Journal of Distributed Sensor Networks. 2015:590584. doi: 10.1155/2015/590584.

26. Keidel M, Vauth F, Richter J, et al. Homebased telerehabilitation after stroke. Nervenarzt. 2017 Feb;88(2):113-119. doi: 10.1007/s00115-016-0275-x.

27. Rizzo AS, Shilling R. Clinical Virtual Reality tools to advance the prevention, assessment, and treatment of PTSD. Eur J Psychotraumatol. 2017 Jan 16;8(sup5):1414560. doi: 10.1080/20008198.2017.1414560. eCollection 2017.

28. Rothbaum BO, Hodges L, Ready D, et al. Virtual reality exposure therapy for Vietnam veterans with posttraumatic stress disorder. J Clin Psychiatry. 2001 Aug;62(8):617-22.

29. Botella C, Serrano B, Banos RM, GarciaPalacios A. Virtual reality exposure-based therapy for the treatment of post-traumatic stress disorder: a review of its efficacy, the adequacy of the treatment protocol, and its acceptability. Neuropsychiatr Dis Treat. 2015 Oct 3;11:2533-45. doi: 10.2147/NDT.S89542. eCollection 2015.

30. Tjernstro F, Zur O, Jahn K. Current concepts and future approaches to vestibular rehabilitation. J Neurol. 2016 Apr;263 Suppl 1: S65-70. doi: 10.1007/s00415-015-7914-1. Epub 2016 Apr 15.

31. JASON: Artificial Intelligence for Health Care 2017. https://www.healthit.gov/sites/default/files/jsr-17-task-002_aiforhealthandhealthcare12122017.pdf


For citation:


Petukhova N.V., Farkhadov M.P., Zamegrad M.V., Grachev S.P. Digital technologies in the diagnosis and treatment of neurological diseases. Neurology, Neuropsychiatry, Psychosomatics. 2019;11(4):104-110. (In Russ.) https://doi.org/10.14412/2074-2711-2019-4-104-110

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ISSN 2074-2711 (Print)
ISSN 2310-1342 (Online)