Neurosurgery

Deep Learning-Based Intraoperative Stent Graft Segmentation on Completion Digital Subtraction Angiography During Endovascular Aneurysm Repair.

PURPOSE: Modern endovascular hybrid operating rooms generate large amounts of medical images during a procedure, which are currently mostly assessed by eye. In this paper, we present fully automatic segmentation of the stent graft on the completion digital subtraction angiography during endovascular aneurysm repair, utilizing a deep learning network.

Deep Learning-Based Magnetic Resonance Imaging in Diagnosis and Treatment of Intracranial Aneurysm.

This study was focused on the positioning of the intracranial aneurysm in the magnetic resonance imaging (MRI) images using the deep learning-based U-Net model, to realize the computer-aided diagnosis of the intracranial aneurysm. First, a network was established based on the three-dimensional (3D) U-Net model, and the collected image data were input into the network to realize the automatic locat...

Single-Cell Sequencing Analysis and Multiple Machine Learning Methods Identified G0S2 and HPSE as Novel Biomarkers for Abdominal Aortic Aneurysm.

Identifying biomarkers for abdominal aortic aneurysms (AAA) is key to understanding their pathogenesis, developing novel targeted therapeutics, and po...

Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1 min of video.

Major vascular injury resulting in uncontrolled bleeding is a catastrophic and often fatal complication of minimally invasive surgery. At the outset o...

Computed Tomography Images under Artificial Intelligence Algorithms on the Treatment Evaluation of Intracerebral Hemorrhage with Minimally Invasive Aspiration.

The aim of this study was to investigate the therapeutic effect of minimally invasive aspiration on intracerebral hemorrhage (ICH) and the value of ar...

Automatic hemorrhage segmentation on head CT scan for traumatic brain injury using 3D deep learning model.

The most common cause of long-term disability and death in young adults is a traumatic brain injury. The decision for surgical intervention for cranio...

Deep Learning and Microscopic Imaging in the Nursing Process of Neurosurgery Operation.

Neurosurgery is mainly for the treatment of head trauma, cerebrovascular disease, brain tumors, and spinal cord disorders. These operations are diffic...

Stereotactic Neurosurgical Robotics With Real-Time Patient Tracking: A Cadaveric Study.

BACKGROUND: Robotic neurosurgery may improve the accuracy, speed, and availability of stereotactic procedures. We recently developed a computer vision...

Telerobotic neurovascular interventions with magnetic manipulation.

Advances in robotic technology have been adopted in various subspecialties of both open and minimally invasive surgery, offering benefits such as enha...

Dense, deep learning-based intracranial aneurysm detection on TOF MRI using two-stage regularized U-Net.

BACKGROUND AND PURPOSE: The prevalence of unruptured intracranial aneurysms in the general population is high and aneurysms are usually asymptomatic. ...

Establishment of a Combined Diagnostic Model of Abdominal Aortic Aneurysm with Random Forest and Artificial Neural Network.

OBJECTIVES: Abdominal aortic aneurysm (AAA), a disease with high mortality, is limited by the current diagnostic methods in the early screening. This ...

Deep Learning-based Automated Aortic Area and Distensibility Assessment: the Multi-Ethnic Study of Atherosclerosis (MESA).

This study details application of deep learning for automatic segmentation of the ascending and descending aorta from 2D phase-contrast cine magnetic ...

A robotic magnetic resonance-guided high-intensity focused ultrasound platform for neonatal neurosurgery: Assessment of targeting accuracy and precision in a brain phantom.

BACKGROUND: Intraventricular hemorrhage (IVH) is one of the most serious neurovascular complications resulting from premature birth. It can result in ...

Virtual Reality in the Neurosciences: Current Practice and Future Directions.

Virtual reality has made numerous advancements in recent years and is used with increasing frequency for education, diversion, and distraction. Beginn...

Deep Learning in the Management of Intracranial Aneurysms and Cerebrovascular Diseases: A Review of the Current Literature.

Intracranial aneurysms are a common asymptomatic vascular pathology, the rupture of which is a devastating event with a significant risk of morbidity ...

A simulation study to investigate the use of concentric tube robots for epilepsy surgery.

PURPOSE: Patients with pharmacoresistant refractory epilepsy may require epilepsy surgery to prevent future seizure occurrences. Conventional surgery ...

A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography.

Feasibility assessment and planning of thoracic endovascular aortic repair (TEVAR) require computed tomography (CT)-based analysis of geometric aortic...

Machine Learning Approaches-Driven for Mortality Prediction for Patients Undergoing Craniotomy in ICU.

OBJECTIVES: We aimed to predict the mortality of patients with craniotomy in ICU by using predictive models to extract the high-risk factors leading t...

Artificial intelligence assistance improves the accuracy and efficiency of intracranial aneurysm detection with CT angiography.

PURPOSE: The aim of this study was to evaluate whether a novel head and neck artificial intelligence (AI)-assisted diagnostic system based on a three-...

Uncharted Waters of Machine and Deep Learning for Surgical Phase Recognition in Neurosurgery.

Recent years have witnessed artificial intelligence (AI) make meteoric leaps in both medicine and surgery, bridging the gap between the capabilities o...