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A Review on Artificial Intelligence Applications for Multiple Sclerosis Evaluation and Diagnosis

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Innovations in Bio-Inspired Computing and Applications (IBICA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 649))

Abstract

Multiple Sclerosis is one of the most common diseases of the central nervous system that affects millions of people worldwide. The prediction of this disease is considered a challenge since the symptoms are highly variable as the disease worsens and, as such, it has emerged as a topic that artificial intelligence scientists have tried to challenge.

With the goal of providing a brief review that may serve as a starting point for future researchers on such a deep field, this paper puts forward a summary of artificial intelligence applications for Multiple Sclerosis evaluation and diagnosis. It includes a detailed recap of what Multiple Sclerosis is, the connections between artificial intelligence and the human brain, and a description of the main proposals in this field. It also concludes what the most reliable methods are at the present time, discussing approaches that achieve accuracy values up to 98.8%.

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References

  1. Shryock, R.H.: The development of modern medicine. In: The Development of Modern Medicine, University of Pennsylvania Press (2017)

    Google Scholar 

  2. Braga, D., Madureira, A.M., Coelho, L., Abraham, A.: Neurodegenerative diseases detection through voice analysis. In: International Conference on Hybrid Intelligent Systems, pp. 213–223 (2017)

    Google Scholar 

  3. He, W., Goodkind, D., Kowal, P.R.: An aging world: 2015. United States Census Bureau Washington, DC (2016)

    Google Scholar 

  4. Cunha, B., Gonçalves, L.: An IoMT architecture for patient rehabilitation based on low-cost hardware and interoperability standards. In: Queirós, R., Cunha, B., Fonseca, X. (eds.) Exploring the Convergence of Computer and Medical Science Through Cloud Healthcare, pp. 37–59. Hershey, PA, USA: IGI Global (2023). https://doi.org/10.4018/978-1-6684-5260-8.ch003

  5. Pakarinen, T., Pietilä, J., Nieminen, H.: Prediction of self-perceived stress and arousal based on electrodermal activity.  In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2191–2195 (2019)

    Google Scholar 

  6. Ghasemi, N., Razavi, S., Nikzad, E.: Multiple sclerosis: pathogenesis, symptoms, diagnoses and cell-based therapy. Cell J. 19(1), 1 (2017). https://doi.org/10.22074/CELLJ.2016.4867

  7. Compston, A., Coles, A.: Multiple sclerosis. Lancet 372(9648), 1502–1517 (2008). https://doi.org/10.1016/S0140-6736(08)61620-7

    Article  Google Scholar 

  8. Rudick, R.A., Fisher, E., Lee, J.-C., Simon, J., Jacobs, L.: Use of the brain parenchymal fraction to measure whole brain atrophy in relapsing-remitting MS. Multiple sclerosis collaborative research group. Neurology 53(8), 1698–1704 (1999)

    Google Scholar 

  9. Tommasin, S., Giannì, C., De Giglio, L., Pantano, P.: Neuroimaging techniques to assess inflammation in multiple sclerosis. Neuroscience 403, 4–16 (A. 2019). https://doi.org/10.1016/J.NEUROSCIENCE.2017.07.055

    Article  Google Scholar 

  10. Braga, D., Madureira, A.M., Coelho, L., Ajith, R.: Automatic detection of Parkinson’s disease based on acoustic analysis of speech. Eng. Appl. Artif. Intell. 77, 148–158 (2019)

    Article  Google Scholar 

  11. Nabizadeh, F., et al.: Artificial intelligence in the diagnosis of multiple sclerosis: a systematic review. Mult. Scler. Relat. Disord. 59, 103673 (2022). https://doi.org/10.1016/J.MSARD.2022.103673

  12. McCarthy, J.: What is artificial intelligence. http://www-formal.stanford.edu/jmc/whatisai.html (2004)

  13. Turing, A.M.: Mind. Mind 59(236), 433–460 (1950)

    Article  MathSciNet  Google Scholar 

  14. Cunha, B., Madureira, A., Fonseca, B., Matos, J.: Intelligent scheduling with reinforcement learning. Appl. Sci. 11(8), 3710 (2021)

    Article  Google Scholar 

  15. Cunha, B., Madureira, A.M., Fonseca, B., Coelho, D.: Deep reinforcement learning as a job shop scheduling solver: a literature review BT - hybrid intelligent systems, pp. 350–359 (2020)

    Google Scholar 

  16. IBM Cloud Education: What is Artificial Intelligence (AI)? IBM  (2020)

    Google Scholar 

  17. Cobb, M.: The idea of the brain: the past and future of neuroscience (2020)

    Google Scholar 

  18. Rowe, W.: what is a neural network? an introduction with examples – BMC software | Blogs (2020)

    Google Scholar 

  19. Yuste, R.: From the neuron doctrine to neural networks. Nat. Rev. Neurosci. 16(8), 487–498 (2015). https://doi.org/10.1038/NRN3962

    Article  Google Scholar 

  20. de No, R.L.: Physiology of the nervous system, ed. fulton. Oxford Univ (1949)

    Google Scholar 

  21. Gershenson, C.: (PDF) Artificial neural networks for beginners (2003)

    Google Scholar 

  22. Yang, G.R., Wang, X.J.: Artificial neural networks for neuroscientists: a primer. Neuron 107(6), 1048–1070 (2020). https://doi.org/10.1016/J.NEURON.2020.09.005

    Article  Google Scholar 

  23. Akomolafe, D.T.: (PDF) Scholars research library comparative study of biological and artificial neural networks (2013)

    Google Scholar 

  24. Ray, S.: SVM: Support vector machine algorithm in machine learning (2015)

    Google Scholar 

  25. McKinley, R., et al.: Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks. Sci. Reports 11(1), 1–11 (2021). https://doi.org/10.1038/s41598-020-79925-4

  26. Moazami, F., Lefevre-Utile, A., Papaloukas, C., Soumelis, V.: Machine learning approaches in study of multiple sclerosis disease through magnetic resonance images. Front. Immunol. 12, 3205 (2021). https://doi.org/10.3389/FIMMU.2021.700582/BIBTEX

    Article  Google Scholar 

  27. Rocca, M.A., et al.: Deep learning on conventional magnetic resonance imaging improves the diagnosis of multiple sclerosis mimics. Invest. Radiol. 56(4), 252–260 (2021). https://doi.org/10.1097/RLI.0000000000000735

    Article  Google Scholar 

  28. Lapuschkin, S., Binder, A., Montavon, G., Samek, W.: (PDF) The LRP toolbox for artificial neural networks (2016)

    Google Scholar 

  29. Zaimi, A., Wabartha, M., Herman, V., Antonsanti, P.L., Perone, C.S., Cohen-Adad, J.: AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks. Sci. Reports 8(1), 1–11 (2018). https://doi.org/10.1038/s41598-018-22181-4

  30. Barnett, Y., Garber, J.Y., Barnett, M.H.: MRI biomarkers of disease progression in multiple sclerosis: old dog, new tricks? Quant. Imaging Med. Surg. 10(2), 52732–52532 (2020). https://doi.org/10.21037/QIMS.2020.01.04

    Article  Google Scholar 

  31. Roca, P., et al.: Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI. Diagn. Interv. Imaging 101(12), 795–802 (2020). https://doi.org/10.1016/J.DIII.2020.05.009

    Article  Google Scholar 

  32. Loizou, C.P., Pantzaris, M., Pattichis, C.S.: Normal appearing brain white matter changes in relapsing multiple sclerosis: Texture image and classification analysis in serial MRI scans. Magn. Reson. Imaging 73, 192–202 (2020). https://doi.org/10.1016/J.MRI.2020.08.022

    Article  Google Scholar 

  33. Marzullo, A., et al.: Prediction of multiple sclerosis patient disability from structural connectivity using convolutional neural networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2087–2090 (2019). https://doi.org/10.1109/EMBC.2019.8856845

  34. Ion-Margineanu, A., et al.: Machine learning approach for classifying multiple sclerosis courses by combining clinical data with lesion loads and magnetic resonance metabolic features. Front. Neurosci. 11, 398 (2017). https://doi.org/10.3389/FNINS.2017.00398/BIBTEX

  35. Mato-Abad, V., et al.: Classification of radiologically isolated syndrome and clinically isolated syndrome with machine-learning techniques. Eur. J. Neurol. 26(7), 1000–1005 (2019). https://doi.org/10.1111/ENE.13923

    Article  Google Scholar 

  36. Kim, H., et al.: Deep Learning-Based Method to Differentiate Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis. Front. Neurol. 11, 1642 (2020). https://doi.org/10.3389/FNEUR.2020.599042/BIBTEX

    Article  Google Scholar 

  37. Wang, Z., et al.: 3D compressed convolutional neural network differentiates neuromyelitis optical spectrum disorders from multiple sclerosis using automated white matter hyperintensities segmentations. Front. Physiol. 11, 1736 (2020). https://doi.org/10.3389/FPHYS.2020.612928/BIBTEX

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Porto Research, Technology and Innovation Center, Polytechnic of Porto (PORTIC/IPP), Porto, Portugal

    Bruno Cunha & Lucas Gonçalves

  2. ISRC – ISEP/P.PORTO, Porto, Portugal

    Ana Madureira

Authors
  1. Bruno Cunha
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  2. Ana Madureira
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  3. Lucas Gonçalves
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Corresponding author

Correspondence to Bruno Cunha .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, Flame University, Pune, Maharashtra, India

    Ajith Abraham

  2. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

  4. Interdisciplinary Studies Research Center (ISRC), Institute of Engineering, Polytechnique of Porto (ISEP/P.PORTO), INOV (Institute for Systems and Computer Engineering, Technology and Science), Porto, Portugal

    Ana Maria Madureira

  5. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Türkiye

    Cengiz Kahraman

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Cunha, B., Madureira, A., Gonçalves, L. (2023). A Review on Artificial Intelligence Applications for Multiple Sclerosis Evaluation and Diagnosis. In: Abraham, A., Bajaj, A., Gandhi, N., Madureira, A.M., Kahraman, C. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2022. Lecture Notes in Networks and Systems, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-031-27499-2_35

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