Cerebrospinal fluid volume as an early radiological factor for clinical course prediction after aneurysmal subarachnoid hemorrhage. A pilot study.

BACKGROUND: The pathological mechanisms following aneurysmal subarachnoid hemorrhage (SAH) are poorly understood. Limited clinical evidence exists on the association between cerebrospinal fluid (CSF) volume and the risk of delayed cerebral ischemia (DCI) or cerebral vasospasm (CV). In this study, we raised the hypothesis that the amount of CSF or its ratio to hemorrhage blood volume, as determined from non-contrast Computed Tomography (NCCT) images taken on admission, could be a significant predictor for CV and DCI. METHODS: The pilot study included a retrospective analysis of NCCT scans of 49 SAH patients taken shortly after an aneurysm rupture (33 males, 16 females, mean age 56.4 ± 15 years). The SynthStrip and Slicer3D software tools were used to extract radiological factors - CSF, brain, and hemorrhage volumes from the NCCT images. The "pure" CSF volume (VCSF) was estimated in the range of [-15, 15] Hounsfield units (HU). RESULTS: VCSF was negatively associated with the risk of CV occurrence (p = 0.0049) and DCI (p = 0.0069), but was not associated with patients' outcomes. The hemorrhage volume (VSAH) was positively associated with an unfavorable outcome (p = 0.0032) but was not associated with CV/DCI. The ratio VSAH/VCSF was positively associated with, both, DCI (p = 0.031) and unfavorable outcome (p = 0.002). The CSF volume normalized by the brain volume showed the highest characteristics for DCI prediction (AUC = 0.791, sensitivity = 0.80, specificity = 0.812) and CV prediction (AUC = 0.769, sensitivity = 0.812, specificity = 0.70). CONCLUSION: It was demonstrated that "pure" CSF volume retrieved from the initial NCCT images of SAH patients (including CV, Non-CV, DCI, Non-DCI groups) is a more significant predictor of DCI and CV compared to other routinely used radiological biomarkers. VCSF could be used to predict clinical course as well as to personalize the management of SAH patients. Larger multicenter clinical trials should be performed to test the added value of the proposed methodology.

Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm.

Introduction: Intracranial hemorrhage detection in 3D Computed Tomography (CT) brain images has gained more attention in the research community. The major issue to deal with the 3D CT brain images is scarce and hard to obtain the labelled data with better recognition results. Methods: To overcome the aforementioned problem, a new model has been implemented in this research manuscript. After acquiring the images from the Radiological Society of North America (RSNA) 2019 database, the region of interest (RoI) was segmented by employing Otsu's thresholding method. Then, feature extraction was performed utilizing Tamura features: directionality, contrast, coarseness, and Gradient Local Ternary Pattern (GLTP) descriptors to extract vectors from the segmented RoI regions. The extracted vectors were dimensionally reduced by proposing a modified genetic algorithm, where the infinite feature selection technique was incorporated with the conventional genetic algorithm to further reduce the redundancy within the regularized vectors. The selected optimal vectors were finally fed to the Bi-directional Long Short Term Memory (Bi-LSTM) network to classify intracranial hemorrhage sub-types, such as subdural, intraparenchymal, subarachnoid, epidural, and intraventricular. Results: The experimental investigation demonstrated that the Bi-LSTM based modified genetic algorithm obtained 99.40% sensitivity, 99.80% accuracy, and 99.48% specificity, which are higher compared to the existing machine learning models: Naïve Bayes, Random Forest, Support Vector Machine (SVM), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) network.

Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies.

Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic resonance image (MRI), and electroencephalography (EEG) data were used to evaluate the performance of the developed method. In this paper, various methods such as statistical analysis, logistic regression, machine learning, and deep learning methods were used in the prediction and detection of SAH which are reviewed. The advantages and limitations of SAH prediction and risk assessment methods are also being reviewed. Most of the existing methods were evaluated on the collected dataset for the SAH prediction. In some researches, deep learning methods were applied, which resulted in higher performance in the prediction process. EEG data were applied in the existing methods for the prediction process, and these methods demonstrated higher performance. However, the existing methods have the limitations of overfitting problems, imbalance data problems, and lower efficiency in feature analysis. The artificial neural network (ANN) and support vector machine (SVM) methods have been applied for the prediction process, and considerably higher performance is achieved by using this method.

Convolutional neural network for detection and classification of seizures in clinical data.

Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients suffer from severe detrimental effects like physical injury or depression due to unpredictable seizures. However, even in hospitals due to the high rate of false positives, the seizure alert systems are of poor help for patients as tools of seizure detection are mostly trained on unrealistically clean data, containing little noise and obtained under controlled laboratory conditions, where patient groups are homogeneous, e.g. in terms of age or type of seizures. In this study authors present the approach for detection and classification of a seizure using clinical data of electroencephalograms and a convolutional neural network trained on features of brain synchronisation and power spectrum. Various deep learning methods were applied, and the network was trained on a very heterogeneous clinical electroencephalogram dataset. In total, eight different types of seizures were considered, and the patients were of various ages, health conditions and they were observed under clinical conditions. Despite this, the classifier presented in this paper achieved sensitivity and specificity equal to 0.68 and 0.67, accordingly, which is a significant improvement as compared to the known results for clinical data. Graphical abstract.