Evaluating Deep Learning Techniques for Detecting Aneurysmal Subarachnoid Hemorrhage: A Comparative Analysis of Convolutional Neural Network and Transfer Learning Models.

Objective: Machine learning and deep learning techniques offer a promising multidisciplinary solution for subarachnoid hemorrhage (SAH) detection. The novel transfer learning approach mitigates the time constraints associated with the traditional techniques and demonstrates a superior performance. This study aims to evaluate the effectiveness of convolutional neural networks (CNNs) and CNN-based transfer learning models in differentiating between aneurysmal SAH and nonaneurysmal SAH.

Acute Neurologic Deterioration in Mobile Spinal Schwannoma.

Background: Spinal schwannomas are commonly presented with minor symptoms, including radicular pain, sensory deficits, and minor neurologic deficit. Acute neurologic deterioration is uncommon.

Case Description: In this study, a case of cauda equina schwannoma presented with acute neurologic deficit after movement of spinal schwannoma is presented.