Neurosurgery

Using Predictive Modeling and Machine Learning to Identify Patients Appropriate for Outpatient Anterior Cervical Fusion and Discectomy.

STUDY DESIGN: Retrospective, case-control.

The Path to Surgical Robotics in Neurosurgery.

Robotic systems may help efficiently execute complicated tasks that require a high degree of accurac...

Introduction: Surgical Robotics in Neurosurgery Review Series.

Model Experimental Study of Man-Machine Interactive Robot-Assisted Craniotomy.

To evaluate the feasibility, safety, and accuracy of the new man-machine interactive robotic system ...

Artificial Intelligence in Clinical Neurosurgery: More than Machinery.

Predicting Discharge Disposition Following Meningioma Resection Using a Multi-Institutional Natural Language Processing Model.

BACKGROUND: Machine learning (ML)-based predictive models are increasingly common in neurosurgery, b...

Two Hundred Twenty-Six Consecutive Deep Brain Stimulation Electrodes Placed Using an "Asleep" Technique and the Neuro|MateTM Robot for the Treatment of Movement Disorders.

BACKGROUND: Robotics in neurosurgery has demonstrated widening indications and rapid growth in recen...

Robot-Assisted Instrumented Fusion of a T8-9 Extension Distraction Fracture and Epidural Hematoma Evacuation: 2-Dimensional Operative Video.

The utilization of robotics has been gaining increased popularity in spine surgery. It can be used t...

Promises and Perils of Artificial Intelligence in Neurosurgery.

Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prev...

Semiautomated Approach for Muscle Weakness Detection in Clinical Texts.

The automated detection of adverse events in medical records might be a cost-effective solution for ...

Classification of Intracranial Hemorrhage Subtypes Using Deep Learning on CT Scans.

Intracranial hemorrhage is a pathological condition that requires fast diagnosis and decision making...

Big Data, Big Impact: The Potential for Data Science in Neurosurgery.

[Artificial intelligence in neurosurgery].

BACKGROUND: Artificial intelligence (AI) in neurosurgery is becoming increasingly more important as ...

Letter to the Editor: "Bridging Machine Learning and Clinical Practice in Neurosurgery: Hurdles and Solutions".

Therapeutic approach comparison in bicuspid aortic valve aortopathy and clinical practice implications.

Bicuspid aortic valve (BAV) is the most common heart valve malformation, and it may be associated wi...

[Pulmonary nodule detection method based on convolutional neural network].

A method was proposed to detect pulmonary nodules in low-dose computed tomography (CT) images by two...

Convolutional Neural Networks for the Detection and Measurement of Cerebral Aneurysms on Magnetic Resonance Angiography.

Aneurysm size correlates with rupture risk and is important for treatment planning. User annotation ...

An Information Extraction Algorithm for Detecting Adverse Events in Neurosurgery Using Documents Written in a Natural Rich-in-Morphology Language.

Rich-in-morphology language, such as Russian, present a challenge for extraction of professional med...

Representation Learning of 3D Brain Angiograms, an Application for Cerebral Vasospasm Prediction.

Stroke is the fifth leading cause of death in the United States. Subarachnoid hemorrhage (SAH) is a ...

Prediction of Postoperative Hospital Stay with Deep Learning Based on 101 654 Operative Reports in Neurosurgery.

Electronic Health Records (EHRs) conceal a hidden knowledge that could be mined with data science to...

Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving ...