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Artificial Intelligence in Health                        Complex early diagnosis of MS through machine learning



            21.  Piri Cinar B, Ozakbas S. Prediction of conversion from   31.  Banerjee T, Saha M, Ghosh E, et al. Conversion of clinically
               clinically isolated syndrome to multiple sclerosis according   isolated syndrome to multiple sclerosis: A  prospective
               to baseline characteristics: A  prospective study.  Noro   multi-center study in Eastern India. Mult Scler J Exp Transl
               Psikiyatr Ars. 2018;55:15-21.                      Clin. 2019;5(2):205521731984972.
               doi: 10.29399/npa.12667                            doi: 10.1177/2055217319849721
            22.  Shaheen HA, Sayed SS, Daker LI, Taha MA. Early predictors   32.  Rommer PS, Milo R, Han MH, et al. Immunological aspects
               of conversion in patients with clinically isolated syndrome:   of approved MS therapeutics. Front Immunol. 2019;10:1564.
               A  preliminary  Egyptian  study.  Egypt J Neurol Psychiatr      doi: 10.3389/fimmu.2019.01564
               Neurosurg. 2018;54(1):21.
                                                               33.  Pinto MF, Oliveira H, Batista S, et al. Prediction of disease
               doi: 10.1186/s41983-018-0021-3
                                                                  progression and outcomes in  multiple sclerosis with
            23.  Bi CF, Qian HR, Peng LJ, et al. The correlation factor analysis   machine learning. Sci Rep. 2020;10(1):21038.
               for conversion of clinically isolated syndrome to multiple      doi: 10.1038/s41598-020-78212-6
               sclerosis and neuromyelitis optica. Zhonghua Nei Ke Za Zhi.
               2016;55(6):460-465.                             34.  Zhao Y, Healy BC, Rotstein D, et al. Exploration of machine
                                                                  learning techniques in predicting multiple sclerosis disease
               doi: 10.3760/cma.j.issn.0578-1426.2016.06.012
                                                                  course. PLoS One. 2017;12(4):e0174866.
            24.  Kuhle J, Disanto G, Dobson R,  et al. Conversion from      doi: 10.1371/journal.pone.0174866
               clinically isolated syndrome to multiple sclerosis: A  large
               multicentre study. Mult Scler. 2015;21(8):1013-1024.  35.  Ion-Mărgineanu A, Kocevar G, Stamile C,  et al. Machine
                                                                  learning approach for classifying multiple sclerosis courses
               doi: 10.1177/1352458514568827
                                                                  by combining clinical data with lesion loads and magnetic
            25.  CHAMPS Study Group. MRI predictors of early conversion   resonance metabolic features. Front Neurosci. 2017;11:398.
               to clinically definite MS in the CHAMPS placebo group.      doi: 10.3389/fnins.2017.00398
               Neurology. 2002;59(7):998-1005.
                                                               36.  Wottschel  V,  Alexander  DC,  Kwok  PP,  et al.  Predicting
               doi: 10.1212/WNL.59.7.998
                                                                  outcome  in clinically  isolated  syndrome  using  machine
            26.  Alroughani R, Al Hashel J, Lamdhade S, Ahmed SF.   learning. Neuroimage Clin. 2015;7:281-287.
               Predictors of conversion to multiple sclerosis in patients      doi: 10.1016/j.nicl.2014.11.021
               with clinical isolated syndrome using the 2010 revised
               McDonald criteria. ISRN Neurol. 2012;2012:792192.  37.  Jasperse B, Barkhof F. Machine Learning in Multiple Sclerosis.
                                                                  United States: Humana Press Inc.; 2023. p. 899-919.
               doi: 10.5402/2012/792192
                                                                  doi: 10.1007/978-1-0716-3195-9_28
            27.  Kolčava J, Kočica J, Hulová M, et al. Conversion of clinically
               isolated syndrome to multiple sclerosis: A prospective study.   38.  Branco D, di Martino B, Esposito A, Tedeschi G,
               Mult Scler Relat Disord. 2020;44:102262.           Bonavita S, Lavorgna L.  Machine learning techniques for
                                                                  prediction of multiple sclerosis progression.  Soft Comput.
               doi: 10.1016/j.msard.2020.102262
                                                                  2022;26(22):12041-12055.
            28.  Zhang H, Alberts E, Pongratz V, et al. Predicting conversion      doi: 10.1007/s00500-022-07503-z
               from clinically isolated syndrome to multiple sclerosis-an
               imaging-based machine learning approach.  Neuroimage   39.  Haouam KD, Benmalek M. Machine learning algorithms
               Clin. 2019;21:101593.                              for early prediction of multiple sclerosis progression:
                                                                  A  comparative study.  Adv Artif Intell Mach Learn.
               doi: 10.1016/j.nicl.2018.11.003
                                                                  2024;04(01):2027-2051.
            29.  Bendfeldt K, Taschler B, Gaetano L,  et al. MRI-based
               prediction of conversion from clinically isolated syndrome      doi: 10.54364/AAIML.2024.41116
               to clinically definite multiple sclerosis using SVM and lesion   40.  Vázquez-Marrufo M, Sarrias-Arrabal E, García-Torres M,
               geometry. Brain Imaging Behav. 2019;13(5):1361-1374.  Martín-Clemente R, Izquierdo G. A  systematic review of
                                                                  the application of machine-learning algorithms in multiple
               doi: 10.1007/s11682-018-9942-9
                                                                  sclerosis. Neurología (Engl Ed). 2023;38(8):577-590.
            30.  Yoo Y, Tang LYW, Li DKB, et al. Deep learning of brain lesion
               patterns and user-defined clinical and MRI features for      doi: 10.1016/j.nrleng.2020.10.013
               predicting conversion to multiple sclerosis from clinically   41.  Naji Y, Mahdaoui M, Klevor R, Kissani N. Artificial
               isolated syndrome. Comput Methods Biomech Biomed Eng   intelligence and multiple sclerosis: Up-to-date review.
               Imaging Vis. 2019;7(3):250-259.                    Cureus. 2023;15:e45412.
               doi: 10.1080/21681163.2017.1356750                 doi: 10.7759/cureus.45412


            Volume 1 Issue 4 (2024)                        121                               doi: 10.36922/aih.4255
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