Automated feature extraction for predicting multiple sclerosis patient disability using brain MRI
Predicting Multiple Sclerosis (MS) patient's disability level is an important issue as this could help in better diagnoses and monitoring the progression of the disease. Expanded Disability Status Scale (EDSS) is a common protocol used to manually score the disability level. However, it is time...
| Main Authors: | , , , , |
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| Format: | Article |
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The Science and Information SAI Organization
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/100495/ |
| _version_ | 1848863338428104704 |
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| author | Muslim, Ali M. Mashohor, Syamsiah Mahmud, Rozi Al Gawwam, Gheyath Hanafi, Marsyita |
| author_facet | Muslim, Ali M. Mashohor, Syamsiah Mahmud, Rozi Al Gawwam, Gheyath Hanafi, Marsyita |
| author_sort | Muslim, Ali M. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Predicting Multiple Sclerosis (MS) patient's disability level is an important issue as this could help in better diagnoses and monitoring the progression of the disease. Expanded Disability Status Scale (EDSS) is a common protocol used to manually score the disability level. However, it is time-consuming requires expert knowledge and exposure to inter-and intra-subject variation. Many previous studies focused on predicting patients' disability from multiple MRI scans and manual or semi-automated features extraction. Furthermore, all of them are required patient follow up. This study aims to predict MS patients' disability using fully automated feature extraction, single MRI scan, single MRI protocols and without patient follow-up. Data from 65 MS patients were used in this study. They were collected from multiple centers in Iraq and Saudi Arabia. Automated brain abnormalities segmentation, automated brain lobes, and brain periventricular are segmentation have been used to extract large scan features. A linear regression algorithm has been used to predict different types of MS patient disability. Initially, weak performance was found until MS patients were divided into four groups according to the MRI-Tesla model and the condition of the patient with a lesion in the spinal cord or not. The best performance was with an average RMSE of 0.6 to predict the EDSS with a step of 2. These results demonstrate the possibility of predicting with fully automated feature extraction, single MRI scan, single MRI protocols and without patient follow-up. |
| first_indexed | 2025-11-15T13:31:20Z |
| format | Article |
| id | upm-100495 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:31:20Z |
| publishDate | 2022 |
| publisher | The Science and Information SAI Organization |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1004952023-11-24T09:06:06Z http://psasir.upm.edu.my/id/eprint/100495/ Automated feature extraction for predicting multiple sclerosis patient disability using brain MRI Muslim, Ali M. Mashohor, Syamsiah Mahmud, Rozi Al Gawwam, Gheyath Hanafi, Marsyita Predicting Multiple Sclerosis (MS) patient's disability level is an important issue as this could help in better diagnoses and monitoring the progression of the disease. Expanded Disability Status Scale (EDSS) is a common protocol used to manually score the disability level. However, it is time-consuming requires expert knowledge and exposure to inter-and intra-subject variation. Many previous studies focused on predicting patients' disability from multiple MRI scans and manual or semi-automated features extraction. Furthermore, all of them are required patient follow up. This study aims to predict MS patients' disability using fully automated feature extraction, single MRI scan, single MRI protocols and without patient follow-up. Data from 65 MS patients were used in this study. They were collected from multiple centers in Iraq and Saudi Arabia. Automated brain abnormalities segmentation, automated brain lobes, and brain periventricular are segmentation have been used to extract large scan features. A linear regression algorithm has been used to predict different types of MS patient disability. Initially, weak performance was found until MS patients were divided into four groups according to the MRI-Tesla model and the condition of the patient with a lesion in the spinal cord or not. The best performance was with an average RMSE of 0.6 to predict the EDSS with a step of 2. These results demonstrate the possibility of predicting with fully automated feature extraction, single MRI scan, single MRI protocols and without patient follow-up. The Science and Information SAI Organization 2022 Article PeerReviewed Muslim, Ali M. and Mashohor, Syamsiah and Mahmud, Rozi and Al Gawwam, Gheyath and Hanafi, Marsyita (2022) Automated feature extraction for predicting multiple sclerosis patient disability using brain MRI. International Journal of Advanced Computer Science and Applications(IJACSA), 13 (3). 441 - 448. ISSN 2158-107X; ESSN: 2156-5570 https://thesai.org/Publications/ViewPaper?Volume=13&Issue=3&Code=IJACSA&SerialNo=53 10.14569/IJACSA.2022.0130353 |
| spellingShingle | Muslim, Ali M. Mashohor, Syamsiah Mahmud, Rozi Al Gawwam, Gheyath Hanafi, Marsyita Automated feature extraction for predicting multiple sclerosis patient disability using brain MRI |
| title | Automated feature extraction for predicting multiple sclerosis patient disability using brain MRI |
| title_full | Automated feature extraction for predicting multiple sclerosis patient disability using brain MRI |
| title_fullStr | Automated feature extraction for predicting multiple sclerosis patient disability using brain MRI |
| title_full_unstemmed | Automated feature extraction for predicting multiple sclerosis patient disability using brain MRI |
| title_short | Automated feature extraction for predicting multiple sclerosis patient disability using brain MRI |
| title_sort | automated feature extraction for predicting multiple sclerosis patient disability using brain mri |
| url | http://psasir.upm.edu.my/id/eprint/100495/ http://psasir.upm.edu.my/id/eprint/100495/ http://psasir.upm.edu.my/id/eprint/100495/ |