Diagnostic Techniques and Artificial Intelligence for Multiple Sclerosis Identification
Introduction: Multiple sclerosis is a chronic autoimmune disorder causing the degeneration of the myelin sheath, affecting nerve signal transmission. Symptoms include muscle weakness, visual disturbances, balance impairments, and incoordination. Early diagnosis is crucial for effective disease mana...
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Ilam University of Medical Sciences
2025-07-01
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| Series: | Majallah-i Dānishgāh-i ’Ulūm-i Pizishkī-i Īlām |
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| Online Access: | http://sjimu.medilam.ac.ir/article-1-8525-en.pdf |
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| author | Asma Raisi Mahsa Nasiri Hajar Danesh |
| author_facet | Asma Raisi Mahsa Nasiri Hajar Danesh |
| author_sort | Asma Raisi |
| collection | DOAJ |
| description | Introduction: Multiple sclerosis is a chronic autoimmune disorder causing the degeneration of the myelin sheath, affecting nerve signal transmission. Symptoms include muscle weakness, visual disturbances, balance impairments, and incoordination. Early diagnosis is crucial for effective disease management and preventing irreversible neurological damage. This research was designed to explore diagnostic methods and introduces machine learning for automated data analysis and faster diagnosis.
Materials & Methods: This study reviewed diagnostic methods for multiple sclerosis (MS), including electroencephalography (EEG), electromyography (EMG), clinical data, cerebrospinal fluid analysis, magnetic resonance imaging (MRI), and optical coherence tomography (OCT). Artificial intelligence (AI)-based approaches were also introduced to enable automated data analysis and expedite disease diagnosis. A novel platform-based method was proposed as an exclusive approach for automated detection through the integration of established diagnostic techniques.
Results: Findings indicated that magnetic resonance imaging (MRI) demonstrates high accuracy in the diagnosis of multiple sclerosis. Based on the average performance of artificial intelligence-based methods across the primary diagnostic modalities, accuracies of 90%, 75%, 80%, 90%, and 95% were achieved for MRI, optical coherence tomography (OCT), electroencephalography (EEG), electromyography (EMG), and cerebrospinal fluid analysis, respectively. The proposed platform integrates these modalities to enhance both the speed and accuracy of disease detection.
Conclusion: The utilization of advanced diagnostic techniques, coupled with the integration of multiple methodologies, markedly improves the early detection and therapeutic intervention of multiple sclerosis, thereby reducing the associated complications of the disease. |
| format | Article |
| id | doaj-art-e81d97ad2b904fdeb1aa71d452a59458 |
| institution | Kabale University |
| issn | 1563-4728 2588-3135 |
| language | fas |
| publishDate | 2025-07-01 |
| publisher | Ilam University of Medical Sciences |
| record_format | Article |
| series | Majallah-i Dānishgāh-i ’Ulūm-i Pizishkī-i Īlām |
| spelling | doaj-art-e81d97ad2b904fdeb1aa71d452a594582025-08-25T07:35:14ZfasIlam University of Medical SciencesMajallah-i Dānishgāh-i ’Ulūm-i Pizishkī-i Īlām1563-47282588-31352025-07-01333113148Diagnostic Techniques and Artificial Intelligence for Multiple Sclerosis IdentificationAsma Raisi0Mahsa Nasiri1Hajar Danesh2 Dept of Electrical and Biomedical Engineering, Faculty of Engineering and Technology, Shahid Ashrafi Esfahani University, Isfahan, Iran Dept of Electrical and Biomedical Engineering, Faculty of Engineering and Technology, Shahid Ashrafi Esfahani University, Isfahan, Iran Dept of Electrical and Biomedical Engineering, Faculty of Engineering and Technology, Shahid Ashrafi Esfahani University, Isfahan, Iran Introduction: Multiple sclerosis is a chronic autoimmune disorder causing the degeneration of the myelin sheath, affecting nerve signal transmission. Symptoms include muscle weakness, visual disturbances, balance impairments, and incoordination. Early diagnosis is crucial for effective disease management and preventing irreversible neurological damage. This research was designed to explore diagnostic methods and introduces machine learning for automated data analysis and faster diagnosis. Materials & Methods: This study reviewed diagnostic methods for multiple sclerosis (MS), including electroencephalography (EEG), electromyography (EMG), clinical data, cerebrospinal fluid analysis, magnetic resonance imaging (MRI), and optical coherence tomography (OCT). Artificial intelligence (AI)-based approaches were also introduced to enable automated data analysis and expedite disease diagnosis. A novel platform-based method was proposed as an exclusive approach for automated detection through the integration of established diagnostic techniques. Results: Findings indicated that magnetic resonance imaging (MRI) demonstrates high accuracy in the diagnosis of multiple sclerosis. Based on the average performance of artificial intelligence-based methods across the primary diagnostic modalities, accuracies of 90%, 75%, 80%, 90%, and 95% were achieved for MRI, optical coherence tomography (OCT), electroencephalography (EEG), electromyography (EMG), and cerebrospinal fluid analysis, respectively. The proposed platform integrates these modalities to enhance both the speed and accuracy of disease detection. Conclusion: The utilization of advanced diagnostic techniques, coupled with the integration of multiple methodologies, markedly improves the early detection and therapeutic intervention of multiple sclerosis, thereby reducing the associated complications of the disease.http://sjimu.medilam.ac.ir/article-1-8525-en.pdfmultiple sclerosismriocteegartificial intelligence |
| spellingShingle | Asma Raisi Mahsa Nasiri Hajar Danesh Diagnostic Techniques and Artificial Intelligence for Multiple Sclerosis Identification Majallah-i Dānishgāh-i ’Ulūm-i Pizishkī-i Īlām multiple sclerosis mri oct eeg artificial intelligence |
| title | Diagnostic Techniques and Artificial Intelligence for Multiple Sclerosis Identification |
| title_full | Diagnostic Techniques and Artificial Intelligence for Multiple Sclerosis Identification |
| title_fullStr | Diagnostic Techniques and Artificial Intelligence for Multiple Sclerosis Identification |
| title_full_unstemmed | Diagnostic Techniques and Artificial Intelligence for Multiple Sclerosis Identification |
| title_short | Diagnostic Techniques and Artificial Intelligence for Multiple Sclerosis Identification |
| title_sort | diagnostic techniques and artificial intelligence for multiple sclerosis identification |
| topic | multiple sclerosis mri oct eeg artificial intelligence |
| url | http://sjimu.medilam.ac.ir/article-1-8525-en.pdf |
| work_keys_str_mv | AT asmaraisi diagnostictechniquesandartificialintelligenceformultiplesclerosisidentification AT mahsanasiri diagnostictechniquesandartificialintelligenceformultiplesclerosisidentification AT hajardanesh diagnostictechniquesandartificialintelligenceformultiplesclerosisidentification |