Systematic ReviewSystematic Review
Open Access

Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis

Tareef S. Daqqaq, Ayman S. Alhasan and Hadeel A. Ghunaim
Neurosciences Journal April 2024, 29 (2) 77-89; DOI: https://doi.org/10.17712/nsj.2024.2.20230103

References

  1. 1.↵
    1. Walton C,
    2. King R,
    3. Rechtman L,
    4. Kaye W,
    5. Leray E,
    6. Marrie RA, et al.
    Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition. Mult Scler déc 2020; 26: 1816–1821.
  2. 2.↵
    1. Howard J,
    2. Trevick S,
    3. Younger DS.
    Epidemiology of Multiple Sclerosis. Neurologic Clinics 2016; 34: 919–939.
  3. 3.↵
    1. Calabresi PA.
    Diagnosis and management of multiple sclerosis. Am Fam Physician 2004; 70: 1935–1944.
    OpenUrlPubMedWeb of Science
  4. 4.↵
    1. Eshaghi A,
    2. Young AL,
    3. Wijeratne PA,
    4. Prados F,
    5. Arnold DL,
    6. Narayanan S, et al.
    Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat Commun 2021; 12: 2078.
    OpenUrlCrossRef
  5. 5.↵
    1. Hartmann M,
    2. Fenton N,
    3. Dobson R.
    Current review and next steps for artificial intelligence in multiple sclerosis risk research. Comput Biol Med 2021; 132: 104337.
    OpenUrl
  6. 6.↵
    1. Sawcer S,
    2. Franklin RJM,
    3. Ban M.
    Multiple sclerosis genetics. Lancet Neurol 2014; 13: 700–709.
    OpenUrlCrossRefPubMedWeb of Science
  7. 7.↵
    1. Landfeldt E,
    2. Castelo-Branco A,
    3. Svedbom A,
    4. Löfroth E,
    5. Kavaliunas A,
    6. Hillert J.
    The long-term impact of early treatment of multiple sclerosis on the risk of disability pension. J Neurol 2018; 265: 701–707.
    OpenUrlPubMed
  8. 8.↵
    1. Buyukturkoglu K,
    2. Zeng D,
    3. Bharadwaj S,
    4. Tozlu C,
    5. Mormina E,
    6. Igwe KC, et al.
    Classifying multiple sclerosis patients on the basis of SDMT performance using machine learning. Mult Scler 2021; 27: 107–116.
    OpenUrl
  9. 9.↵
    1. Filippi M,
    2. Rocca MA,
    3. Ciccarelli O,
    4. De Stefano N,
    5. Evangelou N,
    6. Kappos L, et al.
    MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol 2016; 15: 292–303.
    OpenUrlCrossRefPubMed
  10. 10.↵
    1. Richards TL.
    Proton MR spectroscopy in multiple sclerosis: value in establishing diagnosis, monitoring progression, and evaluating therapy. AJR Am J Roentgenol 1991; 157: 1073–1078.
    OpenUrlCrossRefPubMedWeb of Science
  11. 11.↵
    1. Filippi M,
    2. Rocca MA.
    Magnetization Transfer Magnetic Resonance Imaging in the Assessment of Neurological Diseases. J Neuroimaging 2004; 14: 303–313.
    OpenUrlCrossRefPubMedWeb of Science
  12. 12.↵
    1. Rovaris M,
    2. Gass A,
    3. Bammer R,
    4. Hickman SJ,
    5. Ciccarelli O,
    6. Miller DH, et al.
    Diffusion MRI in multiple sclerosis. Neurology 2005; 65: 1526–1532.
    OpenUrlCrossRefPubMed
  13. 13.↵
    1. Lapointe E,
    2. Li DKB,
    3. Traboulsee AL,
    4. Rauscher A.
    What Have We Learned from Perfusion MRI in Multiple Sclerosis? AJNR Am J Neuroradiol 2018; 39: 994–1000.
    OpenUrlAbstract/FREE Full Text
  14. 14.↵
    1. Shoeibi A,
    2. Khodatars M,
    3. Jafari M,
    4. Moridian P,
    5. Rezaei M,
    6. Alizadehsani R, et al.
    Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review. Comput Biol Med 2021; 136: 104697.
    OpenUrl
  15. 15.↵
    1. Górriz JM,
    2. Ramírez J,
    3. Ortíz A,
    4. Martínez-Murcia FJ,
    5. Segovia F,
    6. Suckling J, et al.
    Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications. Neurocomputing 2020; 410: 237–270.
    OpenUrl
  16. 16.↵
    1. Martinez-Murcia FJ,
    2. Górriz JM,
    3. Ramírez J,
    4. Ortiz A.
    Convolutional Neural Networks for Neuroimaging in Parkinson’s Disease: Is Preprocessing Needed? Int J Neural Syst 2018; 28: 1850035.
    OpenUrl
  17. 17.↵
    1. Sadeghi D,
    2. Shoeibi A,
    3. Ghassemi N,
    4. Moridian P,
    5. Khadem A,
    6. Alizadehsani R, et al.
    An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works. Comput Biol Med 2022: 146: 105554.
    OpenUrl
  18. 18.↵
    1. Jiménez-Mesa C,
    2. Ramírez J,
    3. Suckling J,
    4. Vöglein J,
    5. Levin J,
    6. Górriz JM, et al.
    Deep Learning in current Neuroimaging: a multivariate approach with power and type I error control but arguable generalization ability. ArXiv 2103: 16685.
  19. 19.↵
    1. McKinley R,
    2. Wepfer R,
    3. Aschwanden F,
    4. Grunder L,
    5. Muri R,
    6. Rummel C, et al.
    Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks. Sci Rep 2021; 11: 1087.
    OpenUrlCrossRefPubMed
  20. 20.↵
    1. Page MJ,
    2. McKenzie JE,
    3. Bossuyt PM,
    4. Boutron I,
    5. Hoffmann TC,
    6. Mulrow CD, et al.
    The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021 29; 372.
    OpenUrl
  21. 21.↵
    1. Whiting PF.
    QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies. Ann Intern Med 2011; 155: 529–536.
  22. 22.↵
    1. Borenstein M,
    2. Hedges LV,
    3. Higgins JPT,
    4. Rothstein HR.
    A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Method 2010; 1: 97–111.
    OpenUrlCrossRef
  23. 23.↵
    1. Afzal H,
    2. Luo S,
    3. Ramadan S,
    4. Lechner-Scott J,
    5. Li J.
    Automatic Prediction of the Conversion of Clinically Isolated Syndrome to Multiple Sclerosis Using Deep Learning. In: International conference on video and image processing. 2018: 231–235. From: https://doi.org/10.1145/3301506.3301526
  24. 24.↵
    1. Afzal HM,
    2. Luo S,
    3. Ramadan S,
    4. Lechner-Scott J,
    5. Amin MR,
    6. Li J, et al.
    Automatic and Robust Segmentation of Multiple Sclerosis Lesions with Convolutional Neural Networks. Computers, Materials & Continua 2021; 66: 1.
    OpenUrl
  25. 25.↵
    1. Alijamaat A
    , NikravanShalmani A, Bayat P. Multiple sclerosis identification in brain MRI images using wavelet convolutional neural networks. Int J Imaging Syst Technol 2021; 31: 778–785.
    OpenUrl
  26. 26.↵
    1. Aslani S,
    2. Dayan M,
    3. Storelli L,
    4. Filippi M,
    5. Murino V,
    6. Rocca MA, et al.
    Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. Neuroimage 2019; 196: 1–15.
    OpenUrl
  27. 27.↵
    1. Crimi A,
    2. Bakas S,
    3. Kuijf H,
    4. Keyvan F,
    5. Reyes M,
    6. van Walsum T, éditeurs.
    7. Brainlesion: Glioma
    1. Aslani S,
    2. Dayan M,
    3. Murino V,
    4. Sona D.
    Deep 2D Encoder-Decoder Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation in Brain MRI. In: Crimi A, Bakas S, Kuijf H, Keyvan F, Reyes M, van Walsum T, éditeurs. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Internet). Cham: Springer International Publishing; 2019 (cité 6 déc 2021). p. 132–41. (Lecture Notes in Computer Science; vol. 11383). Disponible sur: http://link.springer.com/10.1007/978-3-030-11723-8_13
  28. 28.↵
    1. Coronado I,
    2. Gabr RE,
    3. Narayana PA.
    Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis. Mult Scler 2021; 27: 519–527.
    OpenUrlCrossRefPubMed
  29. 29.↵
    1. Eitel F,
    2. Soehler E,
    3. Bellmann-Strobl J,
    4. Brandt AU,
    5. Ruprecht K,
    6. Giess RM, et al.
    Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation. Neuroimage Clin 2019; 24: 102003.
    OpenUrl
  30. 30.↵
    1. Kazancli E,
    2. Prchkovska V,
    3. Rodrigues P,
    4. Villoslada P,
    5. Igual L.
    Multiple Sclerosis Lesion Segmentation using Improved Convolutional Neural Networks: In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Internet). Funchal, Madeira, Portugal: SCITEPRESS - Science and Technology Publications; 2018 (cité 6 déc 2021). p. 260–9. Disponible sur: http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006540902600269
  31. 31.
    1. Crimi A,
    2. Bakas S,
    3. Kuijf H,
    4. Keyvan F,
    5. Reyes M,
    6. van Walsum T
    1. La Rosa F,
    2. Fartaria MJ,
    3. Kober T,
    4. Richiardi J,
    5. Granziera C,
    6. Thiran JP, et al.
    Shallow vs Deep Learning Architectures for White Matter Lesion Segmentation in the Early Stages of Multiple Sclerosis. In: Crimi A, Bakas S, Kuijf H, Keyvan F, Reyes M, van Walsum T, éditeurs. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Internet). Cham: Springer International Publishing; 2019 (cité 6 déc 2021). p. 142–51. (Lecture Notes in Computer Science; vol. 11383). Disponible sur: http://link.springer.com/10.1007/978-3-030-11723-8_14
  32. 32.↵
    1. Roy S,
    2. Butman JA,
    3. Reich DS,
    4. Calabresi PA,
    5. Pham DL.
    Multiple sclerosis lesion segmentation from brain MRI via fully convolutional neural networks. ArXiv 2018; 1803.09172.
    OpenUrl
  33. 33.↵
    1. Shrwan R,
    2. Gupta A.
    Classification of Pituitary Tumor and Multiple Sclerosis Brain Lesions through Convolutional Neural Networks. IOP Conf Ser: Mater Sci Eng 2021; 1049: 012014.
  34. 34.↵
    1. Siar H,
    2. Teshnehlab M.
    Diagnosing and classification tumors and MS simultaneous of magnetic resonance images using convolution neural network. In2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS). IEEE: Piscataway (NJ) USA; 2019. p. 1–4.
  35. 35.↵
    1. Valverde S,
    2. Salem M,
    3. Cabezas M,
    4. Pareto D,
    5. Vilanova JC,
    6. Ramió-Torrentà L, et al.
    One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks. NeuroImage: Clinical 2019; 21: 101638.
    OpenUrl
  36. 36.↵
    1. Wang SH,
    2. Tang C,
    3. Sun J,
    4. Yang J,
    5. Huang C,
    6. Phillips P, et al.
    Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling. Front Neurosci 2018; 12: 818.
    OpenUrl
  37. 37.↵
    1. Zhang YD,
    2. Pan C,
    3. Sun J,
    4. Tang C.
    Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU. Journal of Computational Science 2018; 28: 1–10.
    OpenUrl
  38. 38.↵
    1. Zhao Y,
    2. Guo S,
    3. Luo M,
    4. Shi X,
    5. Bilello M,
    6. Zhang S, et al.
    A level set method for multiple sclerosis lesion segmentation. Magn Reson Imaging 2018; 49: 94–100.
    OpenUrl
  39. 39.↵
    1. Lladó X,
    2. Oliver A,
    3. Cabezas M,
    4. Freixenet J,
    5. Vilanova JC,
    6. Quiles A, et al.
    Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches. Information Sciences 2012; 186: 164–185.
    OpenUrlCrossRef
  40. 40.↵
    1. Anwar SM,
    2. Majid M,
    3. Qayyum A,
    4. Awais M,
    5. Alnowami M,
    6. Khan MK.
    Medical Image Analysis using Convolutional Neural Networks: A Review. J Med Syst 2018; 42: 226.
    OpenUrlCrossRefPubMed
  41. 41.↵
    1. Danelakis A,
    2. Theoharis T,
    3. Verganelakis DA.
    Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging. Comput Med Imaging Graph 2018; 70: 83–100.
    OpenUrlCrossRefPubMed
  42. 42.↵
    1. Ghribi O,
    2. Sellami L,
    3. Ben Slima M,
    4. Ben Hamida A,
    5. Mhiri C,
    6. Mahfoudh KB.
    An Advanced MRI Multi-Modalities Segmentation Methodology Dedicated to Multiple Sclerosis Lesions Exploration and Differentiation. IEEE Trans Nanobioscience 2017; 16: 656–665.
    OpenUrlPubMed
  43. 43.↵
    1. Ullah Z,
    2. Farooq MU,
    3. Lee SH,
    4. An D.
    A hybrid image enhancement based brain MRI images classification technique. Med Hypotheses 2020; 143: 109922.
    OpenUrl
  44. 44.↵
    1. McKinley R,
    2. Wepfer R,
    3. Grunder L,
    4. Aschwanden F,
    5. Fischer T,
    6. Friedli C, et al.
    Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence. NeuroImage: Clinical 2020; 25: 102104.
    OpenUrl
  45. 45.↵
    1. Narayana PA,
    2. Coronado I,
    3. Sujit SJ,
    4. Wolinsky JS,
    5. Lublin FD,
    6. Gabr RE.
    Deep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Noncontrast MRI. Radiology 2020; 294: 398–404.
    OpenUrlCrossRefPubMed
  46. 46.↵
    1. Sander L,
    2. Pezold S,
    3. Andermatt S,
    4. Amann M,
    5. Meier D,
    6. Wendebourg MJ, et al.
    Accurate, rapid and reliable, fully automated MRI brainstem segmentation for application in multiple sclerosis and neurodegenerative diseases. Hum Brain Mapp 2019; 40: 4091–4104.
    OpenUrl
  47. 47.↵
    1. Wang Z,
    2. Smith CD,
    3. Liu J.
    Ensemble of multi-sized FCNs to improve white matter lesion segmentation. Springer International Publishing 2018; 11046: 223–232.
    OpenUrl
  48. 48.↵
    1. Imrey PB.
    Limitations of Meta-analyses of Studies With High Heterogeneity. JAMA Netw Open 2020; 3: e1919325.
    OpenUrl
Back to top

In this issue

Print
Download PDF
Email Article
Citation Tools
Bookmark this article

Jump to section