Brain herniation on computed tomography is a poor predictor of whether patients with a devastating brain injury can be confirmed dead using neurological criteria

Background It is unclear if the presence of compartmental brain herniation on neuroimaging should be a prerequisite to the clinical confirmation of death using neurological criteria. The World Brain Death Project has posed this as a research question. Methods The final computed tomography of the head scans before death of 164 consecutive patients confirmed dead using neurological criteria and 41 patients with devastating brain injury who died following withdrawal of life sustaining treatment were assessed by a neuroradiologist to compare the incidence of herniation and other features of cerebral swelling. Results There was no difference in the incidence of herniation in patients confirmed dead using neurological criteria and those with devastating brain injury (79% vs 76%, OR 1.23 95%, CI 0.56–2.67). The sensitivity and specificity of brain herniation in patients confirmed dead using neurological criteria was 79% and 24%, respectively. The positive and negative predictive value was 81% and 23%, respectively. The most sensitive computed tomography of the head findings for death using neurological criteria were diffuse sulcal effacement (93%) and basal cistern effacement (91%) and the most specific finding was loss of grey-white differentiation (80%). The only features with a significantly different incidence between the death using neurological criteria group and the devastating brain injury group were loss of grey-white differentiation (46 vs 20%, OR 3.56, 95% CI 1.55–8.17) and presence of contralateral ventricular dilatation (24 vs 44%, OR 0.41, 95% CI 0.20–0.84). Conclusions Neuroimaging is essential in establishing the cause of death using neurological criteria. However, the presence of brain herniation or other signs of cerebral swelling are poor predictors of whether a patient will satisfy the clinical criteria for death using neurological criteria or not. The decision to test must remain a clinical one.

Keep S’Myelin: World Brain Day 2021 Editorial for Residents and Junior Doctors Page—Neurology International MDPI

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Barriers to the Use of Neurologic Criteria to Declare Death in Africa

There are varying medical, legal, social, religious and philosophical perspectives about the distinction between life and death. Death can be declared using cardiopulmonary or neurologic criteria throughout much of the world. After solicitation of brain death/death by neurologic criteria (BD/DNC) protocols from contacts around the world, we found that the percentage of countries with BD/DNC protocols is much lower in Africa than other developing regions. We performed an informal review of the literature to identify barriers to declaration of BD/DNC in Africa. We found that there are numerous medical, legal, social and religious barriers to the creation of BD/DNC protocols in Africa including 1) limited number of healthcare facilities, critical care resources and clinicians with relevant expertise; 2) absence of a political and legal framework codifying death; and 3) cultural and religious perspectives that present ideological conflict with the idea of BD/DNC, in particular, and between traditional and Western medicine, in general. Because there are a number of unique barriers to the creation of BD/DNC protocols in Africa, it remains to be seen how the World Brain Death Project, which is intended to create minimum standards for BD/DNC around the world, will impact BD/DNC determination in Africa.

A Reliable and an Efficient Approach for Diagnosis of Brain Tumor Using Transfer Learning

Introduction: The World Brain Tumor Day is seen on eighth June, in a year. Despite exhaustive research in the medical field, the prevalence of this deadly disease is increasing globally with over new 28,000 braintumor cases being reported annually, in India alone. Recent advancements in the field of machine learning facilitate minimally invasive, efficient and reliable procedures for the diagnosis of Brain tumor. Objective: This research intends to design and devlop a reliable framework for accurate diagnosis of brain tumor mainly meningioma type, gliomatype and pituitary cerebrum tumor utilizing Magnetic Resonance Imaging (MRI), one of the most mainstream non-obtrusive procedure Methods: In the proposed system, pre-trained AlexNet is used to classify meningioma, glioma and pituitary brain tumor. The concept of transfer learning is applied using AlexNet for extracting the features from brain MRI images.The AlexNet contains eight layers in which the first five are convolution layer and the remaining three are fully connected layers. The last layer is a softmax layer which gets the output from fully connected layers. The ReLU non-linearity is applied to the output of every convolution and fully connected layer. The idea of transfer learning is applied utilizing AlexNet for computing thefeatures from brain MRI pictures. The AlexNet contains eight layers in which the initial five are convolution layers and the staying three layers are fully connected layers. a softmax is the last layer , which is feeded by the fully connected layers. The ReLUactivation function is applied to the output of convolution layer and fully connectedlayer Result: The proposed system framework recorded the best order precision of 100 % to classify the brain tumor when validated using a practical dataset. Conclusion: The proposed work presents accurate and automatic brain tumor classification using transfer learning. The features extracted using AlexNet has proven to be efficient in obtaining good discriminative power in diagnosis of brain tumor