scholarly journals “Within a minute” detection of focal cortical dysplasia

2021 ◽  
Author(s):  
Horst Urbach ◽  
Marcel Heers ◽  
Dirk-Matthias Altenmueller ◽  
Andreas Schulze-Bonhage ◽  
Anke Maren Staack ◽  
...  
Keyword(s):  
Neural Network ◽  
False Positive ◽  
Focal Cortical Dysplasia ◽  
Area Under The Curve ◽  
Cortical Dysplasia ◽  
Receiver Operating Curve ◽  
Healthy Controls ◽  
Periventricular Nodular Heterotopia ◽  
Probability Map ◽  
Nodular Heterotopia

Abstract Purpose To evaluate a MRI postprocessing tool for the enhanced and rapid detection of focal cortical dysplasia (FCD). Methods MP2RAGE sequences of 40 consecutive, so far MRI-negative patients and of 32 healthy controls were morphometrically analyzed to highlight typical FCD features. The resulting morphometric maps served as input for an artificial neural network generating a FCD probability map. The FCD probability map was inversely normalized, co-registered to the MPRAGE2 sequence, and re-transferred into the PACS system. Co-registered images were scrolled through “within a minute” to determine whether a FCD was present or not. Results Fifteen FCD, three subcortical band heterotopias (SBH), and one periventricular nodular heterotopia were identified. Of those, four FCD and one SBH were only detected by MRI postprocessing while one FCD and one focal polymicrogryia were missed, respectively. False-positive results occurred in 21 patients and 22 healthy controls. However, true positive cluster volumes were significantly larger than volumes of false-positive clusters (p < 0.001). The area under the curve of the receiver operating curve was 0.851 with a cut-off volume of 0.05 ml best indicating a FCD. Conclusion Automated MRI postprocessing and presentation of co-registered output maps in the PACS allowed for rapid (i.e., “within a minute”) identification of FCDs in our clinical setting. The presence of false-positive findings currently requires a careful comparison of postprocessing results with conventional MR images but may be reduced in the future using a neural network better adapted to MP2RAGE images.

scholarly journals Alterations of structural connectivity and structural co-variance network in focal cortical dysplasia

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dong Ah Lee ◽  
Ho-Joon Lee ◽  
Hyung Chan Kim ◽  
Kang Min Park
Keyword(s):  
Diffusion Tensor ◽  
Focal Cortical Dysplasia ◽  
Brain Mri ◽  
Structural Connectivity ◽  
Cortical Dysplasia ◽  
Clustering Coefficient ◽  
Connectivity Matrix ◽  
Healthy Controls ◽  
Local Efficiency ◽  
Structural Volume

Abstract Background The aim of this study was to investigate alterations in structural connectivity and structural co-variance network in patients with focal cortical dysplasia (FCD). Methods We enrolled 37 patients with FCD and 35 healthy controls. All subjects underwent brain MRI with the same scanner and with the same protocol, which included diffusion tensor imaging (DTI) and T1-weighted imaging. We analyzed the structural connectivity based on DTI, and structural co-variance network based on the structural volume with T1-weighted imaging. We created a connectivity matrix and obtained network measures from the matrix using the graph theory. We tested the difference in network measure between patients with FCD and healthy controls. Results In the structural connectivity analysis, we found that the local efficiency in patients with FCD was significantly lower than in healthy controls (2.390 vs. 2.578, p = 0.031). Structural co-variance network analysis revealed that the mean clustering coefficient, global efficiency, local efficiency, and transitivity were significantly decreased in patients with FCD compared to those in healthy controls (0.527 vs. 0.635, p = 0.036; 0.545 vs. 0.648, p = 0.026; 2.699 vs. 3.801, p = 0.019; 0.791 vs. 0.954, p = 0.026, respectively). Conclusions We demonstrate that there are significant alterations in structural connectivity, based on DTI, and structural co-variance network, based on the structural volume, in patients with FCD compared to healthy controls. These findings suggest that focal lesions with FCD could affect the whole-brain network and that FCD is a network disease.

scholarly journals Comparison of Machine Learning Methods for the Detection of Focal Cortical Dysplasia Lesions: Decision Tree, Support Vector Machine and Artificial Neural Network

2021 ◽  
Author(s):  
Zohreh Ganji ◽  
Seyed Amir Zamanpour ◽  
Hoda Zare
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Focal Cortical Dysplasia ◽  
Normal Subjects ◽  
Cortical Dysplasia ◽  
Support Vector ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Background: Accurate classification of focal cortical dysplasia (FCD) has been challenging due to the problematic visual detection in magnetic resonance imaging (MRI). Hence, recently, there has been a necessity for employing new techniques to solve the problem.Methods: MRI data were collected from 58 participants (30 subjects with FCD type II and 28 normal subjects). Morphological and intensity-based characteristics were calculated for each cortical level and then the performance of the three classifiers: decision tree (DT), support vector machine (SVM) and artificial neural network (ANN) was evaluated.Results: Metrics for evaluating classification methods, sensitivity, specificity and accuracy for the DT were 96.7%, 100% and 98.6%, respectively; It was 95%, 100% and 97.9% for the SVM and 96.7%, 100% and 98.6% for the ANN.Conclusion: Comparison of the performance of the three classifications used in this study showed that all three have excellent performance in specificity, but in terms of classification sensitivity and accuracy, the artificial neural network method has worked better.

scholarly journals Can Electric Nose Breath Analysis Identify Abdominal Wall Hernia Recurrence and Aortic Aneurysms? A Proof-of-Concept Study

2020 ◽  
pp. 155335062091789
Author(s):  
Elwin H. H. Mommers ◽  
Lottie van Kooten ◽  
Simon W. Nienhuijs ◽  
Tammo S. de Vries Reilingh ◽  
Tim Lubbers ◽  
...  
Keyword(s):  
Neural Network ◽  
At Risk ◽  
Aortic Aneurysm ◽  
Area Under The Curve ◽  
Recurrent Hernia ◽  
Hernia Recurrence ◽  
Aortic Aneurysms ◽  
Healthy Controls ◽  
Proof Of Concept ◽  
Patients At Risk

Introduction. This pilot study evaluates if an electronic nose (eNose) can distinguish patients at risk for recurrent hernia formation and aortic aneurysm patients from healthy controls based on volatile organic compound analysis in exhaled air. Both hernia recurrence and aortic aneurysm are linked to impaired collagen metabolism. If patients at risk for hernia recurrence and aortic aneurysms can be identified in a reliable, low-cost, noninvasive manner, it would greatly enhance preventive options such as prophylactic mesh placement after abdominal surgery. Methods. From February to July 2017, a 3-armed proof-of-concept study was conducted at 3 hospitals including 3 groups of patients (recurrent ventral hernia, aortic aneurysm, and healthy controls). Patients were measured once at the outpatient clinic using an eNose with 3 metal-oxide sensors. A total of 64 patients (hernia, n = 29; aneurysm, n = 35) and 37 controls were included. Data were analyzed by an automated neural network, a type of self-learning software to distinguish patients from controls. Results. Receiver operating curves showed that the automated neural network was able to differentiate between recurrent hernia patients and controls (area under the curve 0.74, sensitivity 0.79, and specificity 0.65) as well as between aortic aneurysm patients and healthy controls (area under the curve 0.84, sensitivity 0.83, and specificity of 0.81). Conclusion. This pilot study shows that the eNose can distinguish patients at risk for recurrent hernia and aortic aneurysm formation from healthy controls.

Bilateral periventricular nodular heterotopia with mental retardation and frontonasal malformation

Neurology ◽  
1998 ◽  
Vol 51 (2) ◽  
pp. 499-503 ◽  
Author(s):  
Renzo Guerrini ◽  
William B. Dobyns
Keyword(s):  
Mental Retardation ◽  
Neuronal Migration ◽  
Cortical Dysplasia ◽  
Cognitive Testing ◽  
Facial Features ◽  
Mild Mental Retardation ◽  
Multiple Congenital Anomaly ◽  
Periventricular Nodular Heterotopia ◽  
Nodular Heterotopia ◽  
Mri Study

Background and Objective: Bilateral periventricular nodular heterotopia (BPNH) is a recently recognized malformation of neuronal migration in which nodular masses of gray matter line the walls of the lateral ventricles. Most affected individuals are females with epilepsy and normal intelligence, but no other congenital anomalies. Studies in families with multiple affected individuals, always all females, have mapped one BPNH gene to chromosome Xq28. Several other BPNH syndromes associated with mental retardation and epilepsy but without significant dysmorphic facial features have been observed in males only, which may also be X-linked. This report describes a new syndrome with BPNH.Methods: Clinical and MRI study and cognitive testing of two unrelated boys, aged 8 and 5.5 years, and review of the enlarging spectrum of syndromes associated with BPNH.Results: Similarities between the two boys are sufficient to delineate a new multiple congenital anomaly-mental retardation syndrome that consists of BPNH, regional cortical dysplasia, mild mental retardation, and frontonasal malformation.Conclusions: The cause of this unusual syndrome is unknown; based on linkage of other BPNH syndromes to chromosome Xq28 and the report of possible X-linked inheritance of frontonasal malformation, we suspect the cause is genetic, with possible X-linked inheritance.

scholarly journals Automatic Detection of Focal Cortical Dysplasia Type II in MRI: Is the Application of Surface-Based Morphometry and Machine Learning Promising?

2021 ◽  
Vol 15 ◽  
Author(s):  
Zohreh Ganji ◽  
Mohsen Aghaee Hakak ◽  
Seyed Amir Zamanpour ◽  
Hoda Zare
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Focal Cortical Dysplasia ◽  
Diagnostic System ◽  
Cortical Dysplasia ◽  
Type Ii ◽  
Magnetic Resonance Imaging Mri ◽  
Drug Resistant Epilepsy ◽  
Sensitivity Specificity ◽  
Postsurgical Outcomes

Background and ObjectivesFocal cortical dysplasia (FCD) is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented.MethodsMagnetic resonance imaging (MRI) data were collected from 58 participants (30 with histologically confirmed FCD type II and 28 without a record of any neurological prognosis). Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency.ResultsNeural network evaluation metrics—sensitivity, specificity, and accuracy—were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively.ConclusionAnalyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.

Using a recurrent neural network with S2 characteristics, efficient identification of localised cortical dysplasia

2021 ◽  
pp. 1-14
Author(s):  
A. Karthika ◽  
R. Subramanian ◽  
S. Karthik
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Focal Cortical Dysplasia ◽  
Cortical Dysplasia ◽  
Support Vector ◽  
Sensitivity Rate ◽  
Long Short Term Memory ◽  
Artificial Neural Network Ann ◽  
The Brain

Focal cortical dysplasia (FCD) is an inborn anomaly in brain growth and morphological deformation in lesions of the brain which induces focal seizures. Neurosurgical therapies were performed for the detection of FCD. Furthermore, it can be overcome through the presurgical evaluation of epilepsy. The surgical result is attained basically through the output of the presurgical output. In preprocessing the process of increasing true positives with the decrease in false negatives occurs which results in an effective outcome. MRI (Magnetic Resonance Imaging) outputs are efficient to predict the FCD lesions through T1- MPRAGE and T2- FLAIR efficient output can be obtained. In our proposed work we extract the S2 features through the testing of T1, T2 images. Using RNN-LSTM (Recurrent neural network-Long short-term memory) test images were trained and the FCD lesions were segmented. The output of our work is compared with the proposed work yields better results compared to the existing system such as artificial neural network (ANN), support vector machine (SVM), and convolution neural network (CNN). This approach obtained an accuracy rate of 0.195% (ANN), 0.20% (SVM), 0.14% (CNN), specificity rate of 0.23% (ANN), 0.15% (SVM), 0.13% (CNN) and sensitivity rate of 0.22% (ANN), 0.14% (SVM), 0.08% (CNN) respectively in comparison with RNN-LSTM.

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