scholarly journals Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images

2021 ◽  
Vol 12 ◽  
Author(s):  
Faezeh Moazami ◽  
Alain Lefevre-Utile ◽  
Costas Papaloukas ◽  
Vassili Soumelis
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Clinical Manifestations ◽  
Magnetic Resonance Images ◽  
Learning Approaches ◽  
Resonance Imaging ◽  
Automated Diagnosis ◽  
Multiple Sclerosis Disease ◽  
Magnetic Resonance Imaging Mri ◽  
Applications Of Machine Learning

Multiple sclerosis (MS) is one of the most common autoimmune diseases which is commonly diagnosed and monitored using magnetic resonance imaging (MRI) with a combination of clinical manifestations. The purpose of this review is to highlight the main applications of Machine Learning (ML) models and their performance in the MS field using MRI. We reviewed the articles of the last decade and grouped them based on the applications of ML in MS using MRI data into four categories: 1) Automated diagnosis of MS, 2) Prediction of MS disease progression, 3) Differentiation of MS stages, 4) Differentiation of MS from similar disorders.

scholarly journals Pulvinar sign in a case of anti-HU paraneoplastic encephalitis

2016 ◽  
Vol 29 (6) ◽  
pp. 436-439 ◽  
Author(s):  
Pierre-Luc Gamache ◽  
Maude-Marie Gagnon ◽  
Martin Savard ◽  
François Émond
Keyword(s):  
Magnetic Resonance Imaging ◽  
Differential Diagnosis ◽  
Magnetic Resonance ◽  
Clinical Manifestations ◽  
Resonance Imaging ◽  
Paraneoplastic Encephalitis ◽  
Jakob Disease ◽  
Magnetic Resonance Imaging Mri ◽  
Work Up

This article reports the case of a 68-year-old patient with anti-HU antibodies paraneoplastic encephalitis. The clinical manifestations were atypical and the paraclinical work-up, notably the magnetic resonance imaging (MRI) showing bilateral posterior thalamic hyperintensities (pulvinar sign), misleadingly pointed towards a variant Creutzfeld–Jakob disease. After presenting the case, the differential diagnosis of the pulvinar sign is discussed along with other important diagnostic considerations.

Magnetic resonance imaging and computerized tomography scanning of herpes simplex encephalitis

1987 ◽  
Vol 67 (4) ◽  
pp. 592-594 ◽  
Author(s):  
Eric W. Neils ◽  
Robert Lukin ◽  
Thomas A. Tomsick ◽  
John M. Tew
Keyword(s):  
Magnetic Resonance Imaging ◽  
Herpes Simplex ◽  
Magnetic Resonance ◽  
Computerized Tomography ◽  
Herpes Simplex Encephalitis ◽  
Ct Scanning ◽  
Magnetic Resonance Images ◽  
Resonance Imaging ◽  
Content Type ◽  
Magnetic Resonance Imaging Mri

✓ The authors present two cases of herpes simplex encephalitis (HSE) in which computerized tomography (CT) scanning and magnetic resonance imaging (MRI) were performed. They also review the literature on the use of these imaging modalities in cases of HSE. The striking changes noted in these cases on T2-weighted magnetic resonance images in comparison to the CT findings suggest that MRI will help speed recognition of nonhemorrhagic HSE abnormalities.

scholarly journals Can MRI quantify the volume changes of denervated facial muscles?

2020 ◽  
Vol 30 (1) ◽  
pp. 144-149 ◽  
Author(s):  
Valeria Mastryukova ◽  
Dirk Arnold ◽  
Daniel Güllmar ◽  
Orlando Guntinas-Lichius ◽  
Gerd Fabian Volk
Keyword(s):  
Magnetic Resonance ◽  
Facial Paralysis ◽  
Magnetic Resonance Images ◽  
Manual Segmentation ◽  
Facial Muscle ◽  
Resonance Imaging ◽  
Volume Changes ◽  
Time Points ◽  
Facial Muscles ◽  
Magnetic Resonance Imaging Mri

Could manual segmentation of magnetic resonance images be used to quantify the effects of transcutaneous electrostimulation and reinnervation of denervated facial muscle? Five patients with unilateral facial paralysis were scanned during the study while receiving a daily surface electrostimulation of the paralytic cheek region, but also after reinnervation. Their facial muscles were identified in 3D (coronal, sagittal, and axial) and segmented in magnetic resonance imaging (MRI) data for in total 28 time points over the 12 months of study. A non-significant trend of increasing muscle volume were detected after reinnervation. MRI is a valuable technique in the facial paralysis research.

Association of Cauda Equina Compression on Magnetic Resonance Images and Clinical Signs in Dogs With Degenerative Lumbosacral Stenosis

2002 ◽  
Vol 38 (6) ◽  
pp. 555-562 ◽  
Author(s):  
Philipp D. Mayhew ◽  
Amy S. Kapatkin ◽  
Jeffrey A. Wortman ◽  
Charles H. Vite
Keyword(s):  
Magnetic Resonance ◽  
Cauda Equina ◽  
Clinical Signs ◽  
Magnetic Resonance Images ◽  
Lumbosacral Spine ◽  
Resonance Imaging ◽  
Cross Sectional ◽  
Cauda Equina Compression ◽  
Magnetic Resonance Imaging Mri ◽  
Time Of Presentation

Magnetic resonance imaging (MRI) was used to examine the lumbosacral spine of 27 dogs with degenerative lumbosacral stenosis. Four normal dogs were also similarly imaged. Compression of the soft-tissue structures within the vertebral canal at the lumbosacral space was assessed in two ways: by measuring dorsoventral diameter on T1-weighted sagittal images and cross-sectional area on transverse images. The severity of the clinical signs was compared to the severity of cauda equina compression. No significant correlation was found. It is concluded that degree of compression as determined by MRI at time of presentation is independent of disease severity.

Clinical Significance of Magnetic Resonance Imaging in Preoperative Planning for Reconstruction of Posterior Tibial Tendon Ruptures

Foot & Ankle ◽  
1992 ◽  
Vol 13 (4) ◽  
pp. 208-214 ◽  
Author(s):  
Stephen Conti ◽  
James Michelson ◽  
Melvin Jahss
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Clinical Success ◽  
Magnetic Resonance Images ◽  
Posterior Tibial Tendon ◽  
Resonance Imaging ◽  
Posterior Tibial ◽  
Surgical Evaluation ◽  
Tendon Ruptures ◽  
Magnetic Resonance Imaging Mri

A retrospective study of attenuated/ruptured posterior tibial tendons was conducted of all patients who underwent tendon reconstruction over a 4-year period. The study comprised 20 feet in 19 patients having an average age of 53.3 years, with an average follow-up of 2 years. Preoperative magnetic resonance images were taken and graded for assignment to one of three magnetic resonance imaging (MRI)-based groups. The surgical grade was determined intraoperatively based on a previously described classification scheme. No medical or rheumatologic conditions predisposing to failure could be identified. Failure was defined as postoperative progression of pain and deformity which required subsequent triple arthrodesis. There were six failures at an average of 14.7 months. Surgical evaluation was not correlated to outcome following reconstruction. MRI grading, however, was predictive of outcome. The superior sensitivity of MRI for detecting intramural degeneration in the posterior tibial tendon that was not obvious at surgery may explain why MRI is better than intraoperative tendon inspection for predicting the outcome of reconstructive surgery. Therefore, it may be helpful to obtain preoperative MRI when this particular reconstruction of the posterior tibial tendon is contemplated, since this provides the best measure of tendon integrity and appears to be the best predictor of clinical success after such surgery.

Functional network connectivity abnormalities in multiple sclerosis: Correlations with disability and cognitive impairment

2017 ◽  
Vol 24 (4) ◽  
pp. 459-471 ◽  
Author(s):  
Maria A Rocca ◽  
Paola Valsasina ◽  
Victoria M Leavitt ◽  
Mariaemma Rodegher ◽  
Marta Radaelli ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
Cognitive Impairment ◽  
Magnetic Resonance ◽  
Wide Spectrum ◽  
Clinical Manifestations ◽  
Network Connectivity ◽  
Resonance Imaging ◽  
T2 Lesions ◽  
Magnetic Resonance Imaging Mri

Objective: To investigate resting state (RS) functional connectivity (FC) abnormalities within the principal brain networks in a large cohort of multiple sclerosis (MS) patients, to define the trajectory of FC changes over disease stages and their relation with clinical and structural magnetic resonance imaging (MRI) measures. Methods: RS functional magnetic resonance imaging (fMRI), clinical, and neuropsychological evaluation were obtained from 215 MS patients and 98 healthy controls. Connectivity abnormalities and correlations with clinical/neuropsychological/imaging measures were evaluated. We analyzed seed-voxel FC with seven major hubs, producing one visual/sensory, one motor, two cognitive, one cerebellar, and two subcortical networks. Results: MS patients showed reduced network average RS FC versus controls in the default-mode network. At regional level, a complex pattern of decreased and increased RS FC was found. Reduced RS FC mainly involved sensorimotor, cognitive, thalamic, and cerebellar networks, whereas increased RS FC involved visual/sensory and subcortical networks. Reduced RS FC correlated with T2 lesions. Reduced thalamic RS FC correlated with better neuropsychological performance, whereas for all remaining networks reduced FC correlated with more severe clinical/cognitive impairment. Conclusion: Increased and decreased RS FC occurs in MS and contributes to a wide spectrum of clinical manifestations. RS FC reduction is related to T2 lesions. Such a paradigm is inverted for the thalamic network.

scholarly journals Identification of potentially painful disc fissures in magnetic resonance images using machine-learning modelling

2021 ◽  
Author(s):  
Lagerstrand Kerstin ◽  
Hebelka Hanna ◽  
Brisby Helerna
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Diagnostic Accuracy ◽  
Negative Control ◽  
Magnetic Resonance Images ◽  
Machine Learning Algorithms ◽  
Pain Model ◽  
Accuracy And Precision ◽  
Magnetic Resonance Imaging Mri ◽  
Very High

Abstract Purpose It is suggested that non-specific low back pain (LBP) can be related to nerve ingrowth along granulation tissue in disc fissures, extending into the outer layers of the annulus fibrosus. Present study aimed to investigate if machine-learning modelling of magnetic resonance imaging (MRI) data can classify such fissures as well as pain, provoked by discography, with plausible accuracy and precision. Methods The study was based on previously collected data from 30 LBP patients (age = 26–64 years, 11 males). Pressure-controlled discography was performed in 86 discs with pain-positive discograms, categorized as concordant pain-response at a pressure ≤ 50 psi and for each patient one negative control disc. The CT-discograms were used for categorization of fissures. MRI values and standard deviations were extracted from the midsagittal part and from 5 different sub-regions of the discs. Machine-learning algorithms were trained on the extracted MRI markers to classify discs with fissures extending into the outer annulus or not, as well as to classify discs as painful or non-painful. Results Discs with outer annular fissures were classified in MRI with very high precision (mean of 10 repeated testings: 99%) and accuracy (mean: 97%) using machine-learning modelling, but the pain model only demonstrated moderate diagnostic accuracy (mean accuracy: 69%; precision: 71%). Conclusion The present study showed that machine-learning modelling based on MRI can classify outer annular fissures with very high diagnostic accuracy and, hence, enable individualized diagnostics. However, the model only demonstrated moderate diagnostic accuracy regarding pain that could be assigned to either a non-sufficient model or the used pain reference.

Magnetic Resonance Images of Tendon Microstructure

2000 ◽  
Author(s):  
Rajakumar Israel ◽  
Theresa Atkinson
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Clinical Setting ◽  
Magnetic Resonance Images ◽  
New Method ◽  
Ligament Injury ◽  
Weak Signal ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Abstract Tendon and ligament typically produce a weak signal during Magnetic Resonance Imaging (MRI). As a result only gross defects in the tissue could be detected. A method was recently developed to allow more detailed images of tendon structure to be obtained. This new method requires less than 2.5 minutes per scan and is therefore a reasonable method to utilize in a clinical setting to evaluate tendon or ligament injury and healing.

scholarly journals A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

2020 ◽  
Vol 9 (7) ◽  
pp. 2146
Author(s):  
Gopi Battineni ◽  
Nalini Chintalapudi ◽  
Francesco Amenta ◽  
Enea Traini
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Magnetic Resonance ◽  
Neurodegenerative Disorder ◽  
Support Vector ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.

Reactive Gliosis Simulating Butterfly Glioma: A Neuroradiological Case Study

Neurosurgery ◽  
1986 ◽  
Vol 19 (5) ◽  
pp. 816-819 ◽  
Author(s):  
Cazenave Craig ◽  
Reid Steven ◽  
Virapongse Chat ◽  
Hunter Stephen
Keyword(s):  
Magnetic Resonance ◽  
Malignant Neoplasm ◽  
Magnetic Resonance Images ◽  
Reactive Gliosis ◽  
Resonance Imaging ◽  
Computed Tomographic ◽  
Intractable Seizures ◽  
Computed Tomographic Scan ◽  
Magnetic Resonance Imaging Mri

Abstract Reactive gliosis was found in a 40-year-old man who presented with intractable seizures thought to be due to a malignant neoplasm. Although two separate lesions located bilaterally in the frontal lobes were evident on the computed tomographic scan, a connection between these lesions along the fibers of the corpus callosum was clearly demonstrated on T2-weighted magnetic resonance images. The unusual radiological appearance of this gliosis, which simulated a malignant butterfly glioma on magnetic resonance imaging (MRI), is reported. Because MRI is still a new modality, its images should be interpreted with judicious caution.

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