1. Zoledziewska M. The gut microbiota perspective for interventions in MS. Autoimmunity Reviews. 2019;18(8):814-24. [DOI:10.1016/j.autrev.2019.03.016]

2. Nguyen A-L, Eastaugh A, van der Walt A, Jokubaitis VG. Pregnancy and multiple sclerosis: Clinical effects across the lifespan. Autoimmunity Reviews. 2019;18(10):102360. [DOI:10.1016/j.autrev.2019.102360]

3. Hu Y, Nie H, Yu H-H, Qin C, Wu L-J, Tang Z-P, et al. Efficacy and safety of rituximab for relapsing-remitting multiple sclerosis: A systematic review and meta-analysis. Autoimmunity Reviews. 2019;18(5):542-8. [DOI:10.1016/j.autrev.2019.03.011]

4. Merkel B, Butzkueven H, Traboulsee AL, Havrdova E, Kalincik T. Timing of high-efficacy therapy in relapsing-remitting multiple sclerosis: A systematic review. Autoimmunity Reviews. 2017;16(6):658-65. [DOI:10.1016/j.autrev.2017.04.010]

5. Montes Diaz G, Hupperts R, Fraussen J, Somers V. Dimethyl fumarate treatment in multiple sclerosis: Recent advances in clinical and immunological studies. Autoimmunity Reviews. 2018;17(12):1240-50. [DOI:10.1016/j.autrev.2018.07.001]

6. Ziemssen T, Medin J, Couto CA-M, Mitchell CR. Multiple sclerosis in the real world: A systematic review of fingolimod as a case study. Autoimmunity Reviews. 2017;16(4):355-76. [DOI:10.1016/j.autrev.2017.02.007]

7. Inojosa H, Schriefer D, Ziemssen T. Clinical outcome measures in multiple sclerosis: a review. Autoimmunity reviews. 2020;19(5):102512. [DOI:10.1016/j.autrev.2020.102512]

8. Tremlett H, Marrie RA. The multiple sclerosis prodrome: Emerging evidence, challenges, and opportunities. Multiple Sclerosis Journal. 2021;27(1):6-12. [DOI:10.1177/1352458520914844]

9. Charabati M, Wheeler MA, Weiner HL, Quintana FJ. Multiple sclerosis: Neuroimmune crosstalk and therapeutic targeting. Cell. 2023;186(7):1309-27. [DOI:10.1016/j.cell.2023.03.008]

10. Shah NJ, Abbas Z, Ridder D, Zimmermann M, Oros-Peusquens A-M. A novel MRI-based quantitative water content atlas of the human brain. NeuroImage. 2022;252:119014. [DOI:10.1016/j.neuroimage.2022.119014]

11. Etemadifar M, Izadi S, Nikseresht A, Sharifian M, Sahraian MA, Nasr Z. Estimated prevalence and incidence of multiple sclerosis in Iran. European neurology. 2014;72(5-6):370-4. [DOI:10.1159/000365846]

12. Gafson A, Craner MJ, Matthews PM. Personalised medicine for multiple sclerosis care. Multiple Sclerosis Journal. 2017;23(3):362-9. [DOI:10.1177/1352458516672017]

13. Jelinek G. Overcoming multiple sclerosis: the evidence-based 7 step recovery program: Atlantic Books; 2017.

14. Alrabai A, Echtioui A, Hamida AB, editors. Multiple Sclerosis Segmentation using Deep Learning Models: Comparative Study. 2022 6th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP); 2022: IEEE. [DOI:10.1109/ATSIP55956.2022.9805983]

15. La Rosa F, Abdulkadir A, Fartaria MJ, Rahmanzadeh R, Lu P-J, Galbusera R, et al. Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE. NeuroImage: Clinical. 2020;27:102335. [DOI:10.1016/j.nicl.2020.102335]

16. Aslani S, Murino V, Dayan M, Tam R, Sona D, Hamarneh G, editors. Scanner invariant multiple sclerosis lesion segmentation from MRI. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI); 2020: IEEE. [DOI:10.1109/ISBI45749.2020.9098721]

17. Hempel S, Graham GD, Fu N, Estrada E, Chen AY, Miake-Lye I, et al. A systematic review of modifiable risk factors in the progression of multiple sclerosis. Multiple Sclerosis Journal. 2017;23(4):525-33. [DOI:10.1177/1352458517690270]

18. Soelberg Sorensen P, Giovannoni G, Montalban X, Thalheim C, Zaratin P, Comi G. The multiple sclerosis care unit. Multiple sclerosis journal. 2019;25(5):627-36. [DOI:10.1177/1352458518807082]

19. Biberacher V, Schmidt P, Selter RC, Pernpeinter V, Kowarik MC, Knier B, et al. Fatigue in multiple sclerosis: associations with clinical, MRI and CSF parameters. Multiple Sclerosis Journal. 2018;24(8):1115-25. [DOI:10.1177/1352458517712078]

20. Kjølhede T, Siemonsen S, Wenzel D, Stellmann J-P, Ringgaard S, Pedersen BG, et al. Can resistance training impact MRI outcomes in relapsing-remitting multiple sclerosis? Multiple Sclerosis Journal. 2018;24(10):1356-65. [DOI:10.1177/1352458517722645]

21. Gärtner J, Chitnis T, Ghezzi A, Pohl D, Brück W, Häring DA, et al. Relapse rate and MRI activity in young adult patients with multiple sclerosis: a post hoc analysis of phase 3 fingolimod trials. Multiple Sclerosis Journal-Experimental, Translational and Clinical. 2018;4(2):2055217318778610. [DOI:10.1177/2055217318778610]

22. Fooladi M, Alam NR, Sharini H, Firouznia K, Shakiba M, Harirchian M. Multiparametric qMTI assessment and monitoring of normal appearing white matter and classified T1 hypointense lesions in relapsing-remitting multiple sclerosis. Irbm. 2020;41(3):151-60. [DOI:10.1016/j.irbm.2020.01.004]

23. Barritt AW, Gabel MC, Cercignani M, Leigh PN. Emerging magnetic resonance imaging techniques and analysis methods in amyotrophic lateral sclerosis. Frontiers in neurology. 2018;9:1065. [DOI:10.3389/fneur.2018.01065]

24. Kaunzner UW, Gauthier SA. MRI in the assessment and monitoring of multiple sclerosis: an update on best practice. Therapeutic advances in neurological disorders. 2017;10(6):247-61. [DOI:10.1177/1756285617708911]

25. Ma Y, Zhang C, Cabezas M, Song Y, Tang Z, Liu D, et al. Multiple sclerosis lesion analysis in brain magnetic resonance images: techniques and clinical applications. IEEE Journal of Biomedical and Health Informatics. 2022;26(6):2680-92. [DOI:10.1109/JBHI.2022.3151741]

26. Carass A, Roy S, Jog A, Cuzzocreo JL, Magrath E, Gherman A, et al. Longitudinal multiple sclerosis lesion segmentation: resource and challenge. NeuroImage. 2017;148:77-102. [DOI:10.1016/j.neuroimage.2016.12.064]

27. Malka D, Vegerhof A, Cohen E, Rayhshtat M, Libenson A, Aviv Shalev M, et al. Improved diagnostic process of multiple sclerosis using automated detection and selection process in magnetic resonance imaging. Applied Sciences. 2017;7(8):831. [DOI:10.3390/app7080831]

28. Afzal H, Luo S, Ramadan S, Khari M, Chaudhary G, Lechner-Scott J. Prediction of Conversion from CIS to Clinically Definite Multiple Sclerosis Using Convolutional Neural Networks. Computational and Mathematical Methods in Medicine. 2022;2022. [DOI:10.1155/2022/5154896]

29. Fooladi M, Sharini H, Masjoodi S, Khodamoradi E. A novel classification method using effective neural network and quantitative magnetization transfer imaging of brain white matter in relapsing remitting multiple sclerosis. Journal of biomedical physics & engineering. 2018;8(4):409. [DOI:10.31661/jbpe.v8i4Dec.926]

30. Dacosta-Aguayo R, Genova H, Chiaravalloti ND, DeLuca J. Neuroimaging and rehabilitation in multiple sclerosis. Cognitive Rehabilitation and Neuroimaging: Examining the Evidence from Brain to Behavior. 2020:117-38. [DOI:10.1007/978-3-030-48382-1_6]

31. Zheng Y, Lee JC, Rudick R, Fisher E. Long‐term magnetization transfer ratio evolution in multiple sclerosis white matter lesions. Journal of Neuroimaging. 2018;28(2):191-198. [DOI:10.1111/jon.12480]

32. Moccia M, Ruggieri S, Ianniello A, Toosy A, Pozzilli C, Ciccarelli O. Advances in spinal cord imaging in multiple sclerosis. Therapeutic advances in neurological disorders. 2019;12:1756286419840593. [DOI:10.1177/1756286419840593]

33. Aslani S, Dayan M, Storelli L, Filippi M, Murino V, Rocca MA, et al. Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. NeuroImage. 2019;196:1-15. [DOI:10.1016/j.neuroimage.2019.03.068]

34. Bouman PM, Strijbis VI, Jonkman LE, Hulst HE, Geurts JJ, Steenwijk MD. Artificial double inversion recovery images for (juxta) cortical lesion visualization in multiple sclerosis. Multiple Sclerosis Journal. 2022;28(4):541-9. [DOI:10.1177/13524585211029860]

35. Roca P, Attye A, Colas L, Tucholka A, Rubini P, Cackowski S, et al. Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI. Diagnostic and Interventional Imaging. 2020;101(12):795-802. [DOI:10.1016/j.diii.2020.05.009]

36. Campion T, Smith R, Altmann D, Brito G, Turner B, Evanson J, et al. FLAIR* to visualize veins in white matter lesions: a new tool for the diagnosis of multiple sclerosis? European radiology. 2017;27:4257-63. [DOI:10.1007/s00330-017-4822-z]

37. Acar ZY, Başçiftçi F, Ekmekci AH. A Convolutional Neural Network model for identifying Multiple Sclerosis on brain FLAIR MRI. Sustainable Computing: Informatics and Systems. 2022;35:100706. [DOI:10.1016/j.suscom.2022.100706]

38. Meier DS, Guttmann CR, Tummala S, Moscufo N, Cavallari M, Tauhid S, et al. Dual‐sensitivity multiple sclerosis lesion and CSF segmentation for multichannel 3T brain MRI. Journal of Neuroimaging. 2018;28(1):36-47. [DOI:10.1111/jon.12491]

39. Saccenti L, Andica C, Hagiwara A, Yokoyama K, Takemura MY, Fujita S, et al. Brain tissue and myelin volumetric analysis in multiple sclerosis at 3T MRI with various in-plane resolutions using synthetic MRI. Neuroradiology. 2019;61:1219-27. [DOI:10.1007/s00234-019-02241-w]

40. Roca P, Attye A, Colas L, Tucholka A, Rubini P, Cackowski S, et al. Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI. Diagnostic and Interventional Imaging. 2020;101(12):795-802. [DOI:10.1016/j.diii.2020.05.009]

41. Seccia R, Gammelli D, Dominici F, Romano S, Landi AC, Salvetti M, et al. Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis. PloS one. 2020;15(3):e0230219. [DOI:10.1371/journal.pone.0230219]

42. Alizadehsani R, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Gorriz JM, et al. Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data. ACM Trans Multimedia Comput Commun Appl. 2021;17(3s):Article 104. [DOI:10.1145/3462635]

43. Zurita M, Montalba C, Labbé T, Cruz JP, Dalboni da Rocha J, Tejos C, et al. Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data. NeuroImage: Clinical. 2018;20:724-30. [DOI:10.1016/j.nicl.2018.09.002]

44. Gros C, De Leener B, Badji A, Maranzano J, Eden D, Dupont SM, et al. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. NeuroImage. 2019;184:901-15. [DOI:10.1016/j.neuroimage.2018.09.081]

45. Gessert N, Krüger J, Opfer R, Ostwaldt A-C, Manogaran P, Kitzler HH, et al. Multiple sclerosis lesion activity segmentation with attention-guided two-path CNNs. Computerized Medical Imaging and Graphics. 2020;84:101772. [DOI:10.1016/j.compmedimag.2020.101772]

46. Ebrahimighahnavieh MA, Luo S, Chiong R. Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review. Computer Methods and Programs in Biomedicine. 2020;187:105242. [DOI:10.1016/j.cmpb.2019.105242]

47. Vieira S, Pinaya WHL, Mechelli A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience & Biobehavioral Reviews. 2017;74:58-75. [DOI:10.1016/j.neubiorev.2017.01.002]

48. Janssen RJ, Mourão-Miranda J, Schnack HG. Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2018;3(9):798-808. [DOI:10.1016/j.bpsc.2018.04.004]

49. Martinez-Murcia FJ, Górriz JM, Ramírez J, Ortiz A. Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed? International journal of neural systems. 2018;28(10):1850035. [DOI:10.1142/S0129065718500351]

50. Lin E, Lin CH, Lane HY. Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease. International journal of molecular sciences. 2021;22(15):7911 [DOI:10.3390/ijms22157911]

51. Zaharchuk G, Gong E, Wintermark M, Rubin D, Langlotz CP. Deep Learning in Neuroradiology. AJNR American journal of neuroradiology.2018;39(10):1776-1784. [DOI:10.3174/ajnr.A5543]

52. Shoeibi A, Moridian P, Khodatars M, Ghassemi N, Jafari M, Alizadehsani R, et al. An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works. Computers in biology and medicine. 2022;149:106053. [DOI:10.1016/j.compbiomed.2022.106053]

53. Sui J, Liu M, Lee JH, Zhang J, Calhoun V. Deep learning methods and applications in neuroimaging. Journal of neuroscience methods. 2020;339:108718. [DOI:10.1016/j.jneumeth.2020.108718]

54. Mohammad N, Muad AM, Ahmad R, Yusof M. Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging. BMC medical imaging. 2022;22(1):66. [DOI:10.1186/s12880-022-00794-6]

55. Li C, Xu K, Zhu J, Liu J, Zhang B. Triple Generative Adversarial Networks. IEEE transactions on pattern analysis and machine intelligence. 2022;44(12):9629-40. [DOI:10.1109/TPAMI.2021.3127558]

56. You A, Kim JK, Ryu IH, Yoo TK. Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. Eye and vision (London, England). 2022;9(1):6. [DOI:10.1186/s40662-022-00277-3]

57. Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK. Medical Image Analysis using Convolutional Neural Networks: A Review. J Med Syst. 2018;42(11):226. [DOI:10.1007/s10916-018-1088-1]

58. Cheng J, Tian S, Yu L, Lu H, Lv X. Fully convolutional attention network for biomedical image segmentation. Artificial intelligence in medicine. 2020;107:101899. [DOI:10.1016/j.artmed.2020.101899]

59. Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA. Generative adversarial networks: An overview. IEEE signal processing magazine. 2018;35(1):53-65. [DOI:10.1109/MSP.2017.2765202]

60. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks. Communications of the ACM. 2020;63(11):139-44. [DOI:10.1145/3422622]

61. Aggarwal A, Mittal M, Battineni G. Generative adversarial network: An overview of theory and applications. International Journal of Information Management Data Insights. 2021;1(1):100004. [DOI:10.1016/j.jjimei.2020.100004]

62. Ali H, Biswas MR, Mohsen F, Shah U, Alamgir A, Mousa O, et al. The role of generative adversarial networks in brain MRI: a scoping review. Insights into imaging. 2022;13(1):98. [DOI:10.1186/s13244-022-01237-0]

63. Ronneberger O, Fischer P, Brox T, editors. U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18; 2015: Springer.

64. Gulli A, Pal S. Deep learning with Keras: Packt Publishing Ltd; 2017.

65. Wani MA, Bhat FA, Afzal S, Khan AI. Advances in deep learning: Springer; 2020. [DOI:10.1007/978-981-13-6794-6]

66. Egger C, Opfer R, Wang C, Kepp T, Sormani MP, Spies L, et al. MRI FLAIR lesion segmentation in multiple sclerosis: Does automated segmentation hold up with manual annotation? NeuroImage: Clinical. 2017;13:264-70. [DOI:10.1016/j.nicl.2016.11.020]

67. Tran P, Thoprakarn U, Gourieux E, Dos Santos CL, Cavedo E, Guizard N, et al. Automatic segmentation of white matter hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects. NeuroImage: Clinical. 2022;33:102940. [DOI:10.1016/j.nicl.2022.102940]

68. Schmidt P, Pongratz V, Küster P, Meier D, Wuerfel J, Lukas C, et al. Automated segmentation of changes in FLAIR-hyperintense white matter lesions in multiple sclerosis on serial magnetic resonance imaging. NeuroImage: Clinical. 2019;23:101849. [DOI:10.1016/j.nicl.2019.101849]

69. Neeb H, Boer A, Gliedstein D, Raspe M, Schenk J, editors. Predicting multiple sclerosis from normal appearing brain matter‐combination of quantitative MRI metrics with supervised learning. Proceedings of the World Congress on Engineering and Computer Science; 2014.

70. Neeb H, Schenk J. Multivariate prediction of multiple sclerosis using robust quantitative MR-based image metrics. Zeitschrift für Medizinische Physik. 2019;29(3):262-71. [DOI:10.1016/j.zemedi.2018.10.004]

71. Meena Prakash R, Shantha Selva Kumari R. Spatial fuzzy C means and expectation maximization algorithms with bias correction for segmentation of MR brain images. Journal of medical systems. 2017;41:1-9. [DOI:10.1007/s10916-016-0662-7]

72. Chetih N, Messali Z, Serir A, Ramou N. Robust fuzzy c‐means clustering algorithm using non‐parametric Bayesian estimation in wavelet transform domain for noisy MR brain image segmentation. IET Image Processing. 2018;12(5):652-60. [DOI:10.1049/iet-ipr.2017.0399]

73. Kumar NP, Sriram A, Karuna Y, Saladi S, editors. An improved type 2 fuzzy C means clustering for MR brain image segmentation based on possibilistic approach and rough set theory. 2018 International Conference on Communication and Signal Processing (ICCSP); 2018: IEEE. [DOI:10.1109/ICCSP.2018.8524438]

74. Geng X, Mu Y, Mao S, Ye J, Zhu L. An improved K-means algorithm based on fuzzy metrics. IEEE Access. 2020;8:217416-24. [DOI:10.1109/ACCESS.2020.3040745]

75. Lei T, Jia X, Zhang Y, Liu S, Meng H, Nandi AK. Superpixel-based fast fuzzy C-means clustering for color image segmentation. IEEE Transactions on Fuzzy Systems. 2018;27(9):1753-66. [DOI:10.1109/TFUZZ.2018.2889018]

76. Alam MS, Rahman MM, Hossain MA, Islam MK, Ahmed KM, Ahmed KT, et al. Automatic human brain tumor detection in MRI image using template-based K means and improved fuzzy C means clustering algorithm. Big Data and Cognitive Computing. 2019;3(2):27. [DOI:10.3390/bdcc3020027]

77. Gros C, De Leener B, Badji A, Maranzano J, Eden D, Dupont SM, et al. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuroimage. 2019;184:901-15. [DOI:10.1016/j.neuroimage.2018.09.081]

78. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. nature. 2017;542(7639):115-118. [DOI:10.1038/nature21056]

79. Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Annals of oncology. 2018;29(8):1836-42. [DOI:10.1093/annonc/mdy166]

80. Tschandl P, Rinner C, Apalla Z, Argenziano G, Codella N, Halpern A, et al. Human-computer collaboration for skin cancer recognition. Nature Medicine. 2020;26(8):1229-34. [DOI:10.1038/s41591-020-0942-0]

81. Nabizadeh F, Masrouri S, Ramezannezhad E, Ghaderi A, Sharafi AM, Soraneh S, et al. Artificial intelligence in the diagnosis of multiple sclerosis: A systematic review. Multiple sclerosis and related disorders. 2022;59:103673. [DOI:10.1016/j.msard.2022.103673]

82. Hashemian AH, Manochehri S, Afshari D, Manochehri Z, Salari N, Shahsavari S. Prognosis of multiple sclerosis disease using data mining approaches random forest and support vector machine based on genetic algorithm. Tehran University Medical Journal. 2019;77(1):33-40.

83. Navaderi M, Rajaei S, Rahimirad S, Jafari Harandi A, Ghaleh Z, Falahati K, et al. Identification of Multiple Sclerosis key genetic factors through multi-staged data mining. Multiple sclerosis and related disorders. 2020;39:101446. [DOI:10.1016/j.msard.2019.101446]

84. Leray E, Moreau T, Fromont A, Edan G. Epidemiology of multiple sclerosis. Revue Neurologique. 2016;172(1):3-13. [DOI:10.1016/j.neurol.2015.10.006]

85. Poser S, Kurtzke JF, Poser W, Schlaf G. Survival in multiple sclerosis. Journal of Clinical Epidemiology. 1989;42(2):159-68. [DOI:10.1016/0895-4356(89)90089-9]