scholarly journals Machine learning-optimized Combinatorial MRI scale (COMRISv2) correlates highly with cognitive and physical disability scales in Multiple Sclerosis patients

2021 ◽  
Author(s):  
Erin Kelly ◽  
Mihael Varosanec ◽  
Peter Kosa ◽  
Mary Sandford ◽  
Vesna Prchkovska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Physical Disability ◽  
Brain Mri ◽  
Cognitive Disability ◽  
Mri Imaging ◽  
Tissue Destruction ◽  
Clinical Scales ◽  
Volumetric Features ◽  
Multiple Sclerosis Patients

AbstractComposite MRI scales of central nervous system tissue destruction correlate stronger with clinical outcomes than their individual components in multiple sclerosis (MS) patients. Using machine learning (ML), we previously developed Combinatorial MRI scale (COMRISv1) solely from semi-quantitative (semi-qMRI) biomarkers. Here, we asked how much better COMRISv2 might become with the inclusion of quantitative (qMRI) volumetric features and employment of more powerful ML algorithm.The prospectively acquired MS patients, divided into training (n=172) and validation (n=83) cohorts underwent brain MRI imaging and clinical evaluation. Neurological examination was transcribed to NeurEx app that automatically computes disability scales. qMRI features were computed by LesionTOADS algorithm. Modified random forest pipeline selected biomarkers for optimal model(s) in the training cohort.COMRISv2 models validated moderate correlation with cognitive disability (Rho = 0.674; Linh’s concordance coefficient [CCC] = 0.458; p<0.001) and strong correlations with physical disability (Spearman Rho = 0.830-0.852; CCC = 0.789-0.823; p<0.001). The NeurEx led to the strongest COMRISv2 model. Addition of qMRI features enhanced performance only of cognitive disability model, likely because semi-qMRI biomarkers measure infratentorial injury with greater accuracy.COMRISv2 models predict most granular clinical scales in MS with remarkable criterion validity, expanding scientific utilization of cohorts with missing clinical data.

scholarly journals Assessment of Disability Progression Independent of Relapse and Brain MRI Activity in Patients with Multiple Sclerosis in Poland

2021 ◽  
Vol 10 (4) ◽  
pp. 868
Author(s):  
Katarzyna Kapica-Topczewska ◽  
François Collin ◽  
Joanna Tarasiuk ◽  
Agata Czarnowska ◽  
Monika Chorąży ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Brain Activity ◽  
Brain Mri ◽  
Disability Progression ◽  
Program Monitoring ◽  
Therapeutic Program ◽  
Relapsing Remitting ◽  
Second Year ◽  
Multiple Sclerosis Patients ◽  
Relapsing Remitting Multiple Sclerosis

The aim of the study was to verify the association of clinical relapses and brain activity with disability progression in relapsing/remitting multiple sclerosis patients receiving disease-modifying treatments in Poland. Disability progression was defined as relapse-associated worsening (RAW), progression independent of relapse activity (PIRA), and progression independent of relapses and brain MRI Activity (PIRMA). Data from the Therapeutic Program Monitoring System were analyzed. Three panels of patients were identified: R0, no relapse during treatment, and R1 and R2 with the occurrence of relapse during the first and the second year of treatment, respectively. In the R0 panel, we detected 4.6% PIRA patients at 24 months (p < 0.001, 5.0% at 36 months, 5.6% at 48 months, 6.1% at 60 months). When restricting this panel to patients without brain MRI activity, we detected 3.0% PIRMA patients at 12 months, 4.5% at 24 months, and varying from 5.3% to 6.2% between 36 and 60 months of treatment, respectively. In the R1 panel, RAW was detected in 15.6% patients at 12 months and, in the absence of further relapses, 9.7% at 24 months and 6.8% at 36 months of treatment. The R2 group was associated with RAW significantly more frequently at 24 months compared to the R1 at 12 months (20.7%; p < 0.05), but without a statistical difference later on. In our work, we confirmed that disability progression was independent of relapses and brain MRI activity.

Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis Patients and Controls

2018 ◽  
Vol 31 (3) ◽  
pp. 346-363 ◽  
Author(s):  
Hanni Kiiski ◽  
Lee Jollans ◽  
Seán Ó. Donnchadha ◽  
Hugh Nolan ◽  
Róisín Lonergan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Cognitive Functioning ◽  
Processing Speed ◽  
Multiple Sclerosis Patients

Reduced quality of life among multiple sclerosis patients with sexual disturbance and bladder dysfunction

2001 ◽  
Vol 7 (4) ◽  
pp. 231-235 ◽  
Author(s):  
M W Nortvedt ◽  
T Riise ◽  
K-M Myhr ◽  
A-M Landtblom ◽  
A Bakke ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Multiple Sclerosis ◽  
Physical Disability ◽  
Bladder Dysfunction ◽  
Survey Methods ◽  
Normal Population ◽  
Cross Sectional ◽  
Sf 36 ◽  
Multiple Sclerosis Patients

Objective: Physical disability explains only part of the reduced quality of life found among multiple sclerosis (MS) patients. Bladder dysfunction and sexual disturbance are frequent and distressing problems for MS patients. We therefore estimated the relationship between the presence and degree of sexual disturbance/bladder dysfunction and the patients' quality of life as measured by the SF-36 Health Survey. Methods: We performed a cross-sectional study of all individuals with the onset of MS between 1976 and 1986 in Hordaland County, Norway. The disease duration at examination was 9-19 years; 194 patients (94%) participated. Results: Fifty-three per cent of the patients with low physical disability (Expanded Disability Status Scale (EDSS)≤44.0) reported disease-related sexual disturbance and 44% had bladder dysfunction according to the Incapacity Status Scale. The corresponding figures for the patients with a high physical disability (EDSS>44.0) were 86 and 81% respectively. The patients with sexual disturbance had markedly and significantly reduced scores on all eight SF-36 scales, this was after adjustment for disease development measured by the EDSS. The patients without sexual disturbance scored 0.5 s.d. lower than a normal population on the social functioning scale, whereas those with marked sexual disturbance scored 1.5 s.d. lower. Similar results were found for the patients with bladder dysfunction. Conclusion: Bladder and sexual problems are associated with a marked reduction in the quality of life, also among patients with otherwise low disability. This underlines the need for identifying and treating these problems.

T1 cortical hypointensities and their association with cognitive disability in multiple sclerosis

2010 ◽  
Vol 16 (10) ◽  
pp. 1203-1212 ◽  
Author(s):  
Francesca Bagnato ◽  
Zeena Salman ◽  
Robert Kane ◽  
Sungyoung Auh ◽  
Fredric K Cantor ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Cognitive Impairment ◽  
White Matter ◽  
Statistical Significance ◽  
Verbal Learning ◽  
Lesion Volume ◽  
Cognitive Disability ◽  
3.0 Tesla ◽  
Multiple Sclerosis Patients ◽  
The Impact

Background: Neocortical lesions (NLs) largely contribute to the pathology of multiple sclerosis (MS), although their relevance in patients’ disability remains unknown. Objective: To assess the incidence of T1 hypointense NLs by 3.0-Tesla magnetic resonance imaging (MRI) in patients with MS and examine neocortical lesion association with cognitive impairment. Methods: In this case-control study, 21 MS patients and 21 age-, sex- and years of education-matched healthy volunteers underwent: (i) a neuropsychological examination rating cognitive impairment (Minimal Assessment of Cognitive Function in MS); (ii) a 3.0-Tesla MRI inclusive of an isotropic 1.0 mm3 three-dimensional inversion prepared spoiled gradient-recalled-echo (3D-IRSPGR) image and T1- and T2-weighted images. Hypointensities on 3D-IRSPGR lying in the cortex, either entirely or partially were counted and association between NLs and cognitive impairment investigated. Results: A total of 95 NLs were observed in 14 (66.7%) patients. NL+ patients performed poorer (p = 0.020) than NLpatients only on the delayed recall component of the California Verbal Learning Test. This difference lost statistical significance when a correction for white matter lesion volume was employed. Conclusions: Although T 1 hypointense NLs may be present in a relatively high proportion of multiple sclerosis patients, the impact that they have in cognitive impairment is not independent from white matter disease.

scholarly journals Automated Detection of Multiple Sclerosis Lesions Using Texture-based Features and a Hybrid Classifier

2020 ◽  
Vol 6 (1) ◽  
pp. 16-30
Author(s):  
Somayeh Raiesdana ◽  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Fuzzy Inference ◽  
Brain Mri ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Inference System ◽  
Common Technique ◽  
Magnetic Resonance Imaging Mri

Background: Multiple Sclerosis (MS) is the most frequent non-traumatic neurological disease capable of causing disability in young adults. Detection of MS lesions with magnetic resonance imaging (MRI) is the most common technique. However, manual interpretation of vast amounts of data is often tedious and error-prone. Furthermore, changes in lesions are often subtle and extremely unrepresentative. Objectives: To develop an automated non-subjective method for the detection and quantification of MS lesions. Materials & Methods: This paper focuses on the automatic detection and classification of MS lesions in brain MRI images. Two datasets, one simulated and the other one recorded in hospital, are utilized in this work. A novel hybrid algorithm combining image processing and machine learning techniques is implemented. To this end, first, intricate morphological patterns are extracted from MRI images via texture analysis. Then, statistical textures-based features are extracted. Afterward, two supervised machine learning algorithms, i.e., the Hidden Markov Model (HMM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are employed within a hybrid platform. The hybrid system makes decisions based on ensemble learning. The stacking technique is used to apply predictions from both models o train a perceptron as a decisive model. Results: Experimental results on both datasets indicate that the proposed hybrid method outperforms HMM and ANFIS classifiers with reducing false positives. Furthermore, the performance of the proposed method compared with the state-of-the-art methods, was approved. Conclusion: Remarkable results of the proposed method motivate advanced detection systems employing other MRI sequences and their combination.

scholarly journals Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis

2020 ◽  
Vol 30 (5) ◽  
pp. 674-682 ◽  
Author(s):  
Gabriel Mangeat ◽  
Russell Ouellette ◽  
Maxime Wabartha ◽  
Benjamin De Leener ◽  
Michael Plattén ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Brain Mri

scholarly journals Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Jörn Lötsch ◽  
Susanne Schiffmann ◽  
Katja Schmitz ◽  
Robert Brunkhorst ◽  
Florian Lerch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Serum Concentration ◽  
High Accuracy ◽  
Lipid Mediator ◽  
Multiple Sclerosis Patients ◽  
Concentration Patterns

Quantitative Assessment of Ataxia in Multiple Sclerosis Patients using Spatiotemporal Parameters: A Relief-Based Machine Learning Analysis

2021 ◽  
Author(s):  
Furkan Bilek ◽  
Ferhat Balgetir ◽  
Caner Feyzi Demir ◽  
Gökhan Alkan ◽  
Seda Arslan-Tuncer
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Sensitivity And Specificity ◽  
Classification Performance ◽  
Support Vector ◽  
Learning Methods ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Spatiotemporal Parameters ◽  
Multiple Sclerosis Patients

Abstract Background and Objective Multiple sclerosis (MS) is a chronic, progressive, and autoimmune disease of the central nervous system (CNS) characterized by inflammation, demyelination, and axonal injury. In patients with newly diagnosed MS (ndMS), ataxia can present either as mild or severe and can be difficult to diagnose in the absence of clinical disability. Such difficulties can be eliminated by using decision support systems supported by machine learning methods. The present study aimed to achieve early diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. Materials and Methods The prospective study included 32 ndMS patients with an Expanded Disability Status Scale (EDSS) score of≤2.0 and 32 healthy volunteers. A total of 14 parameters were elicited by using a Win-Track platform. The ndMS patients were differentiated from healthy individuals using multiple classifiers including Artificial Neural Network (ANN), Support Vector Machine (SVM), the k-nearest neighbors (K-NN) algorithm, and Decision Tree Learning (DTL). To improve the performance of the classification, a Relief-based feature selection algorithm was applied to select the subset that best represented the whole dataset. Performance evaluation was achieved based on several criteria such as Accuracy (ACC), Sensitivity (SN), Specificity (SP), and Precision (PREC). Results ANN had a higher classification performance compared to other classifiers, whereby it provided an accuracy, sensitivity, and specificity of 89, 87.8, 90.3% with the use of all parameters and provided the values of 93.7, 96.6%, and 91.1% with the use of parameters selected by the Relief algorithm, respectively. Significance To our knowledge, this is the first study of its kind in the literature to investigate the diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. The proposed method, i. e. Relief-based ANN method, successfully diagnosed ataxia by using a lower number of parameters compared to the numbers of parameters reported in clinical studies, thereby reducing the costs and increasing the performance of the diagnosis. The method also provided higher rates of accuracy, sensitivity, and specificity in the diagnosis of ataxia in ndMS patients compared to other methods. Taken together, these findings indicate that the proposed method could be helpful in the diagnosis of ataxia in minimally impaired ndMS patients and could be a pathfinder for future studies.

Physical Activity in Prevention of Risk and Disability in Some Neurological Diseases

2014 ◽  
Vol 22 (1) ◽  
pp. 21-27
Author(s):  
Józef Opara
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Multiple Sclerosis ◽  
Physical Disability ◽  
Neurological Diseases ◽  
Review Of The Literature ◽  
Disability Progression ◽  
Multiple Sclerosis Patients

Abstract The question of the role of physical activity in preventing disability in neurological diseases is the issue which is not in doubt. There is well known that physical activity in Parkinson`s disease and in Multiple Sclerosis patients is less than is the case in the general population. Numerous scientific studies have confirmed the low physical activity of people with PD and MS. Improving physical activity delays the progress of physical disability and has the effect on increasing the quality of life in those two diseases. In this paper an descriptive review of the literature devoted to the effect of physical activity on risk of PD and its impact on disability progression in PD and MS has been presented. The different recommendations for physical activity and different methods of assessment have been described.

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