№2, 2025
Multiple Sclerosis is a chronic autoimmune disease characterized by heterogeneous clinical manifestations.. In recent years, the application of artificial ıntelligence methods, particularly machine learning and deep learning techniques, has opened new opportunities for multiple sclerosis management. In this study, the potential of artificial ıntelligence for multiple sclerosis diagnostics, treatment and prognosis is estimated.The research found that RNN models excel in long-term disease progression modeling by effectively capturing temporal sequences. Random Forest and XGBoost models accurately predict relapse risks and the probability of disease progression. The MindGlide platform accelerates MRI analysis, while CDSS facilitates the optimization of personalized treatment decisions. Biomarker-based models offer new avenues for early detection of the disease at the subclinical stage. Overall, hybrid model approaches integrating clinical, radiological, and molecular data present a promising pathway for personalized multiple sclerosis management and the development of early intervention strategies (pp.70-85).
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