AIM: To evaluate the technical aspects of the Da Vinci Xi Surgical System in minimally invasive extreme lateral lumbar interbody fusion (XLIF) surgery in a swine model.
AIM: To identify predictors of basilar invagination (BI) prognosis and compare diagnostic properties between logistic modeling and machine learning methods.
AIM: To present an early warning system (EWS) that employs a supervised machine learning algorithm for the rapid detection of extra-axial hematomas (EAHs) in an emergency trauma setting.
AIM: To describe a deep convolutional generative adversarial networks (DCGAN) model which learns normal brain MRI from normal subjects than finds distortions such as a glioma from a test subject while performing a segmentation at the same time.
AIM: To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans.
Current practice of neurosurgery depends on clinical practice guidelines and evidence-based research publications that derive results using statistical methods. However, statistical analysis methods have some limitations such as the inability to anal...
AIM: To identify key determinants of lumbar disc herniation (LDH) patients' satisfaction and to evaluate the efficiency of an artificial neural network (ANN) model to prognosticate satisfaction derived from the hospital stay in this specific patient ...
AIM: To determine the feasibility, advantages, and disadvantages of using a robot for holding and maneuvering the endoscope in transnasal transsphenoidal surgery.