scholarly journals Support Vector Machine-based Spontaneous Intracranial Hypotension Detection on Brain MRI

2021 ◽  
Author(s):  
Philipp G. Arnold ◽  
Emre Kaya ◽  
Marco Reisert ◽  
Niklas Lützen ◽  
Philippe Dovi-Akué ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Intracranial Hypotension ◽  
Brain Mri ◽  
Spontaneous Intracranial Hypotension ◽  
Area Under The Curve ◽  
Support Vector ◽  
Venous Sinuses ◽  
Fully Automatic ◽  
Magnetic Resonance Imaging Mri ◽  
Automatic Algorithm

Abstract Background and Purpose To develop a fully automatic algorithm for the magnetic resonance imaging (MRI) identification of patients with spontaneous intracranial hypotension (SIH). Material and Methods A support vector machine (SVM) was trained with structured reports of 140 patients with clinically suspected SIH. Venous sinuses and basal cisterns were segmented on contrast-enhanced T1-weighted MPRAGE (Magnetization Prepared-Rapid Gradient Echo) sequences using a convolutional neural network (CNN). For the segmented sinuses and cisterns, 56 radiomic features were extracted, which served as input data for the SVM. The algorithm was validated with an independent cohort of 34 patients with proven cerebrospinal fluid (CSF) leaks and 27 patients who had MPRAGE scans for unrelated reasons. Results The venous sinuses and the suprasellar cistern had the best discriminative power to separate SIH and non-SIH patients. On a combined score with 2 points, mean SVM score was 1.41 (±0.60) for the SIH and 0.30 (±0.53) for the non-SIH patients (p < 0.001). Area under the curve (AUC) was 0.91. Conclusion A fully automatic algorithm analyzing a single MRI sequence separates SIH and non-SIH patients with a high diagnostic accuracy. It may help to consider the need of invasive diagnostics and transfer to a SIH center.

scholarly journals Comparisons of clinical characteristics, brain MRI findings, and responses to epidural blood patch between spontaneous intracranial hypotension and post-dural puncture headache: retrospective study

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Gha-Hyun Lee ◽  
Jiyoung Kim ◽  
Hyun-Woo Kim ◽  
Jae Wook Cho
Keyword(s):  
Intracranial Hypotension ◽  
Dural Puncture ◽  
Epidural Blood Patch ◽  
Brain Mri ◽  
Spontaneous Intracranial Hypotension ◽  
Vein Of Galen ◽  
Mri Findings ◽  
Post Dural Puncture Headache ◽  
Straight Sinus ◽  
Blood Patch

Abstract Background Spontaneous intracranial hypotension and post-dural puncture headache are both caused by a loss of cerebrospinal fluid but present with different pathogeneses. We compared these two conditions concerning their clinical characteristics, brain imaging findings, and responses to epidural blood patch treatment. Methods We retrospectively reviewed the records of patients with intracranial hypotension admitted to the Neurology ward of the Pusan National University Hospital between January 1, 2011, and December 31, 2019, and collected information regarding age, sex, disease duration, hospital course, headache intensity, time to the appearance of a headache after sitting, associated phenomena (nausea, vomiting, auditory symptoms, dizziness), number of epidural blood patch treatments, and prognosis. The brain MRI signs of intracranial hypotension were recorded, including three qualitative signs (diffuse pachymeningeal enhancement, venous distention of the lateral sinus, subdural fluid collection), and six quantitative signs (pituitary height, suprasellar cistern, prepontine cistern, mamillopontine distance, the midbrain-pons angle, and the angle between the vein of Galen and the straight sinus). Results A total of 105 patients (61 spontaneous intracranial hypotension patients and 44 post-dural puncture headache patients) who met the inclusion criteria were reviewed. More patients with spontaneous intracranial hypotension required epidural blood patch treatment than those with post-dural puncture headache (70.5% (43/61) vs. 45.5% (20/44); p = 0.01) and the spontaneous intracranial hypotension group included a higher proportion of patients who underwent epidural blood patch treatment more than once (37.7% (23/61) vs. 13.6% (6/44); p = 0.007). Brain MRI showed signs of intracranial hypotension in both groups, although the angle between the vein of Galen and the straight sinus was greater in the post-dural puncture headache group (median [95% Confidence Interval]: 85° [68°-79°] vs. 74° [76°-96°], p = 0.02). Conclusions Patients with spontaneous intracranial hypotension received more epidural blood patch treatments and more often needed multiple epidural blood patch treatments. Although both groups showed similar brain MRI findings, the angle between the vein of Galen and the straight sinus differed significantly between the groups.

Postural Headache In A Patient With Marfan's Syndrome

Cephalalgia ◽  
2003 ◽  
Vol 23 (7) ◽  
pp. 552-555 ◽  
Author(s):  
E Ferrante ◽  
A Citterio ◽  
A Savino ◽  
P Santalucia
Keyword(s):  
Intracranial Hypotension ◽  
Brain Mri ◽  
Spontaneous Intracranial Hypotension ◽  
Csf Leak ◽  
Marfan’S Syndrome ◽  
Marfan's Syndrome ◽  
Postural Headache ◽  
Intracranial Hypotension Syndrome ◽  
Increased Risk ◽  
Diffuse Pachymeningeal Enhancement

A 26-year-old man with Marfan's syndrome had postural headache. Brain MRI with gadolinium showed diffuse pachymeningeal enhancement. MRI myelography revealed bilateral multiple large meningeal diverticula at sacral nerve roots level. He was suspected to have spontaneous intracranial hypotension syndrome. Eight days later headache improved with bed rest and hydration. One month after the onset he was asymptomatic and 3 months later brain MRI showed no evidence of diffuse pachymeningeal enhancement. The 1-year follow-up revealed no neurological abnormalities. The intracranial hypotension syndrome likely resulted from a CSF leak from one of the meningeal diverticula. In conclusion patients with spinal meningeal diverticula (frequently seen in Marfan's syndrome) might be at increased risk of developing CSF leaks, possibly secondary to Valsalva maneuver or minor unrecognizedtrauma.

IMPROVEMENT OF DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS

2018 ◽  
Vol 184 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Gal Amit ◽  
Hanan Datz
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Method ◽  
Algorithm Performance ◽  
Artificial Neural ◽  
First Time ◽  
Automatic Algorithm

Abstract We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and compare its performance with formerly developed support vector machine method. The GC shape of TLD depends on numerous physical parameters, which may significantly affect it. When integrated into a dosimetry laboratory, this automatic algorithm can classify ‘anomalous’ (having any kind of anomaly) GCs for manual review, and ‘regular’ (acceptable) GCs for automatic analysis. The new algorithm performance is then compared with two kinds of formerly developed support vector machine classifiers—regular and weighted ones—using three different metrics. Results show an impressive accuracy rate of 97% for TLD GCs that are correctly classified to either of the classes.

scholarly journals To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Justine Staal ◽  
Francesco Mattace-Raso ◽  
Hennie A. M. Daniels ◽  
Johannes van der Steen ◽  
Johan J. M. Pel
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Predictive Power ◽  
Area Under The Curve ◽  
Classification Performance ◽  
Support Vector ◽  
Potential Biomarker

BackgroundResearch into Alzheimer’s disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer’s disease.MethodHere, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal stage, mild cognitive impairment with underlying the Alzheimer’s disease pathology. The functionality of this network was tested in terms of timing, accuracy, and speed with goal-directed eye-hand tasks. The predictive power was determined by comparing the classification performance of a zero-rule algorithm (baseline), a decision tree, a support vector machine, and a neural network using functional parameters to classify controls without cognitive disorders, mild cognitive impaired patients, and Alzheimer’s disease patients.ResultsFair to good classification was achieved between controls and patients, controls and mild cognitive impaired patients, and between controls and Alzheimer’s disease patients with the support vector machine (77–82% accuracy, 57–93% sensitivity, 63–90% specificity, 0.74–0.78 area under the curve). Classification between mild cognitive impaired patients and Alzheimer’s disease patients was poor, as no algorithm outperformed the baseline (63% accuracy, 0% sensitivity, 100% specificity, 0.50 area under the curve).Comparison with Existing Method(s)The classification performance found in the present study is comparable to that of the existing CSF and MRI biomarkers.ConclusionThe data suggest that visuomotor network dysfunctions have potential in biomarker research and the proposed eye-hand tasks could add to existing tests to form a clear definition of the preclinical phenotype of AD.

scholarly journals Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5483
Author(s):  
Marisol Martinez-Alanis ◽  
Erik Bojorges-Valdez ◽  
Niels Wessel ◽  
Claudia Lerma
Keyword(s):  
Heart Rate ◽  
Support Vector Machine ◽  
Heart Rate Variability ◽  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Area Under The Curve ◽  
Premature Ventricular Complex ◽  
Support Vector ◽  
Auc Value ◽  
Sudden Cardiac Death Risk

Most methods for sudden cardiac death (SCD) prediction require long-term (24 h) electrocardiogram recordings to measure heart rate variability (HRV) indices or premature ventricular complex indices (with the heartprint method). This work aimed to identify the best combinations of HRV and heartprint indices for predicting SCD based on short-term recordings (1000 heartbeats) through a support vector machine (SVM). Eleven HRV indices and five heartprint indices were measured in 135 pairs of recordings (one before an SCD episode and another without SCD as control). SVMs (defined with a radial basis function kernel with hyperparameter optimization) were trained with this dataset to identify the 13 best combinations of indices systematically. Through 10-fold cross-validation, the best area under the curve (AUC) value as a function of γ (gamma) and cost was identified. The predictive value of the identified combinations had AUCs between 0.80 and 0.86 and accuracies between 80 and 86%. Further SVM performance tests on a different dataset of 68 recordings (33 before SCD and 35 as control) showed AUC = 0.68 and accuracy = 67% for the best combination. The developed SVM may be useful for preventing imminent SCD through early warning based on electrocardiogram (ECG) or heart rate monitoring.

The potential of support vector machine as the diagnostic tool for schizophrenia: A systematic literature review of neuroimaging studies

2011 ◽  
Vol 26 (S2) ◽  
pp. 1363-1363 ◽  
Author(s):  
M.P. Collins ◽  
S.E. Pape
Keyword(s):  
Support Vector Machine ◽  
Diagnostic Tool ◽  
Literature Search ◽  
Structural Mri ◽  
Support Vector ◽  
Systematic Literature Search ◽  
Resonance Imaging ◽  
Pubmed Database ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

IntroductionSchizophrenia is a relatively common chronic psychotic mental illness, which usually continues throughout life. Current diagnosis is based on a set of psychiatrist-applied diagnostic criteria. There can be considerable differences between diagnostic classification based upon either the set of criteria used, or the individual who applies the criteria. For this reason, the development of an objective test to inform the diagnosis could be highly beneficial.ObjectivesTo assess the use of Support Vector Machine (SVM) as a potential diagnostic tool for schizophrenia, with a particular focus on the application of SVM to Magnetic Resonance Imaging (MRI) data.AimsTo show the use of SVM on MRI data to be a potentially viable diagnostic test.MethodA systematic literature search was carried out using the PubMed database, Web of Knowledge as well as Google Scholar. This search was conducted using the terms ‘Schizophrenia’, ‘SVM’/‘Support Vector Machine’ and ‘MRI/fMRI’. This was followed by the application of criteria relating to relevance to the desired search topic (as assesed by the author). Ten publications were identified as relevant.ResultsResults showed strong evidence that the application of SVM to MRI data can reliably differentiate between patients with schizophrenia and healthy controls.ConclusionsThe results indicate that using SVM to analyse MRI data can be reliably used to identify schizophrenia, although there is some variability between the results produced. The potential of SVM in application to fMRI (as opposed to structural MRI) data is yet to be fully explored.

scholarly journals Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

2017 ◽  
Vol 131 (13) ◽  
pp. 1465-1481 ◽  
Author(s):  
Víctor González-Castro ◽  
María del C. Valdés Hernández ◽  
Francesca M. Chappell ◽  
Paul A. Armitage ◽  
Stephen Makin ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Human Performance ◽  
Small Vessel Disease ◽  
Brain Mri ◽  
Area Under The Curve ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Svm Classifier ◽  
Perivascular Spaces ◽  
Scale Invariant

In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: (i) statistics obtained from Wavelet transform’s coefficients, (ii) local binary patterns and (iii) bag of visual words (BoW) based descriptors characterizing local keypoints obtained from a dense grid with the scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer 1) and by a trained image analyst (Observer 2). The agreement and cross-correlation between the classifier and Observer 2 (κ = 0.67 (0.58–0.76)) were slightly higher than between the classifier and Observer 1 (κ = 0.62 (0.53–0.72)) and comparable between both the observers (κ = 0.68 (0.61–0.75)). Finally, three logistic regression models using clinical variables as independent variable and each of the PVS ratings as dependent variable were built to assess how clinically meaningful were the predictions of the classifier. The goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values: 0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02 units higher) than that of the model for Observer 2. These results suggest that, although it can be improved, an automatic classifier to assess PVS burden from brain MRI can provide clinically meaningful results close to those from a trained observer.

Diffuse non-aneurysmal SAH in spontaneous intracranial hypotension: Sequela of ventral CSF leak?

Cephalalgia ◽  
2015 ◽  
Vol 36 (6) ◽  
pp. 589-592 ◽  
Author(s):  
Wouter I Schievink ◽  
M Marcel Maya
Keyword(s):  
Cerebral Angiography ◽  
Intracranial Hypotension ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Brain Mri ◽  
Spontaneous Intracranial Hypotension ◽  
Csf Leak ◽  
Superficial Siderosis ◽  
Long Duration ◽  
Spine Mri ◽  
Lateral Sclerosis

Background Spontaneous intracranial hypotension due to a spinal cerebrospinal fluid (CSF) leak has become a well-recognized cause of headaches. Recently, various unusual neurological syndromes have been described in such patients with chronic ventral CSF leaks, including superficial siderosis and an amyotrophic lateral sclerosis-like syndrome. The authors now report two patients with spontaneous intracranial hypotension due to a chronic ventral CSF leak who suffered a diffuse non-aneurysmal subarachnoid hemorrhage (SAH). Description of cases A 62-year-old woman underwent uneventful microsurgical repair of a ventral thoracic CSF leak that had been present for 13 years. Seventeen months after surgery, she was found unresponsive and CT showed a diffuse intracranial SAH. Cerebral angiography and spine and brain MRI did not reveal a source of the SAH. A 73-year-old woman was found unresponsive and CT showed a diffuse intracranial SAH. Cerebral angiography and brain MRI did not reveal a source of the SAH, although superficial siderosis was detected. Spine MRI showed a ventral thoracic CSF leak that by history had been present for 41 years. She underwent uneventful microsurgical repair of the CSF leak. Discussion The authors suggest that patients with a ventral spinal CSF leak of long duration may be at risk of diffuse non-aneurysmal SAH.

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