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 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 Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Arman Eshaghi ◽  
Alexandra L. Young ◽  
Peter A. Wijeratne ◽  
Ferran Prados ◽  
Douglas L. Arnold ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Brain Mri ◽  
Response To Treatment ◽  
Training Dataset ◽  
Multidimensional Data ◽  
Disability Progression ◽  
Unsupervised Machine Learning ◽  
Normal Appearing White Matter ◽  
Positive Treatment

AbstractMultiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials.

scholarly journals Advanced Analysis of Diffusion Tensor Imaging Along With Machine Learning Provides New Sensitive Measures of Tissue Pathology and Intra-Lesion Activity in Multiple Sclerosis

2021 ◽  
Vol 15 ◽  
Author(s):  
Olayinka Oladosu ◽  
Wei-Qiao Liu ◽  
Bruce G. Pike ◽  
Marcus Koch ◽  
Luanne M. Metz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Characteristic Curve ◽  
Brain Mri ◽  
Mean Diffusivity ◽  
High Angular Resolution ◽  
Advanced Analysis ◽  
Single Shell

Tissue pathology in multiple sclerosis (MS) is highly complex, requiring multi-dimensional analysis. In this study, our goal was to test the feasibility of obtaining high angular resolution diffusion imaging (HARDI) metrics through single-shell modeling of diffusion tensor imaging (DTI) data, and investigate how advanced measures from single-shell HARDI and DTI tractography perform relative to classical DTI metrics in assessing MS pathology. We examined 52 relapsing-remitting MS patients who had 3T anatomical brain MRI and DTI. Single-shell HARDI modeling yielded 5 sub-voxel-based metrics, totalling 11 diffusion measures including 4 DTI and 2 tractography metrics. Based on machine learning of 3-dimensional regions of interest, we evaluated the importance of the measures through several tissue classification tasks. These included two within-subject comparisons: lesion versus normal appearing white matter (NAWM); and lesion core versus shell. Further, by stratifying patients as having high (above 75%ile) and low (below 25%ile) number of MS lesions, we also performed 2 classifications between subjects for lesions and NAWM respectively. Results showed that in lesion-NAWM analysis, HARDI orientation distribution function (ODF) energy, DTI fractional anisotropy (FA), and HARDI orientation dispersion index were the top three metrics, which together achieved 65.2% accuracy and 0.71 area under the receiver operating characteristic curve (AUROC). In core-shell analysis, DTI mean diffusivity (MD), radial diffusivity, and FA were the top three metrics, and MD dominated the classification, which achieved 59.3% accuracy and 0.59 AUROC alone. Between patients, FA was the leading feature in lesion comparisons, while ODF energy was the best in NAWM separation. Collectively, single-shell modeling of common diffusion data can provide robust orientation measures of lesion and NAWM pathology, and DTI metrics are most sensitive to intra-lesion abnormality. Combined analysis of both advanced and classical diffusion measures may be critical for improved understanding of MS pathology.

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 Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

Life ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 122
Author(s):  
Ruggiero Seccia ◽  
Silvia Romano ◽  
Marco Salvetti ◽  
Andrea Crisanti ◽  
Laura Palagi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
High Impact ◽  
Prognostic Models ◽  
Early Prediction ◽  
Disease Course ◽  
Slow Progression ◽  
Relapsing Remitting ◽  
Long Time ◽  
Effective Drugs

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.

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.

scholarly journals MicroRNA expression classification for pediatric multiple sclerosis identification

2021 ◽  
Author(s):  
Gabriella Casalino ◽  
Giovanna Castellano ◽  
Arianna Consiglio ◽  
Nicoletta Nuzziello ◽  
Gennaro Vessio
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Expression Profiles ◽  
Healthy Children ◽  
Multifactorial Diseases ◽  
Hyperactivity Disorder ◽  
Pediatric Multiple Sclerosis ◽  
Mirna Expression Profiles ◽  
Selection Algorithms ◽  
Expression Classification

Abstract MicroRNAs (miRNAs) are a set of short non-coding RNAs that play significant regulatory roles in cells. The study of miRNA data produced by Next-Generation Sequencing techniques can be of valid help for the analysis of multifactorial diseases, such as Multiple Sclerosis (MS). Although extensive studies have been conducted on young adults affected by MS, very little work has been done to investigate the pathogenic mechanisms in pediatric patients, and none from a machine learning perspective. In this work, we report the experimental results of a classification study aimed at evaluating the effectiveness of machine learning methods in automatically distinguishing pediatric MS from healthy children, based on their miRNA expression profiles. Additionally, since Attention Deficit Hyperactivity Disorder (ADHD) shares some cognitive impairments with pediatric MS, we also included patients affected by ADHD in our study. Encouraging results were obtained with an artificial neural network model based on a set of features automatically selected by feature selection algorithms. The results obtained show that models developed on automatically selected features overcome models based on a set of features selected by human experts. Developing an automatic predictive model can support clinicians in early MS diagnosis and provide new insights that can help find novel molecular pathways involved in MS disease.

scholarly journals MRI of the Entire Spinal Cord—Worth the While or Waste of Time? A Retrospective Study of 74 Patients with Multiple Sclerosis

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1424
Author(s):  
Esben Nyborg Poulsen ◽  
Anna Olsson ◽  
Stefan Gustavsen ◽  
Annika Reynberg Langkilde ◽  
Annette Bang Oturai ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Spinal Cord ◽  
Cervical Spinal Cord ◽  
Brain Mri ◽  
Anatomical Location ◽  
Spinal Cord Lesions ◽  
Chi Square ◽  
Exact Test ◽  
Mcdonald Criteria ◽  
Chi Square Test

Spinal cord lesions are included in the diagnosis of multiple sclerosis (MS), yet spinal cord MRI is not mandatory for diagnosis according to the latest revisions of the McDonald Criteria. We investigated the distribution of spinal cord lesions in MS patients and examined how it influences the fulfillment of the 2017 McDonald Criteria. Seventy-four patients with relapsing-remitting MS were examined with brain and entire spinal cord MRI. Sixty-five patients received contrast. The number and anatomical location of MS lesions were assessed along with the Expanded Disability Status Scale (EDSS). A Chi-square test, Fischer’s exact test, and one-sided McNemar’s test were used to test distributions. MS lesions were distributed throughout the spinal cord. Diagnosis of dissemination in space (DIS) was increased from 58/74 (78.4%) to 67/74 (90.5%) when adding cervical spinal cord MRI to brain MRI alone (p = 0.004). Diagnosis of dissemination in time (DIT) was not significantly increased when adding entire spinal cord MRI to brain MRI alone (p = 0.04). There was no association between the number of spinal cord lesions and the EDSS score (p = 0.71). MS lesions are present throughout the spinal cord, and spinal cord MRI may play an important role in the diagnosis and follow-up of MS patients.

Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding

2014 ◽  
Vol 115 (3) ◽  
pp. 147-161 ◽  
Author(s):  
Mariano Cabezas ◽  
Arnau Oliver ◽  
Eloy Roura ◽  
Jordi Freixenet ◽  
Joan C. Vilanova ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Brain Mri ◽  
Lesion Detection ◽  
Multiple Sclerosis Lesion
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