Neurosurgery

The role of robotics in modern neurosurgery: current trends and future prospects.

AI-Driven innovations for managing ependymoma in neurosurgery.

AI-Augmented neurosurgery: a new era of ethical challenges.

Artificial intelligence can help detecting incidental intracranial aneurysm on routine brain MRI using TOF MRA data sets and improve the time required for analysis of these images.

PURPOSE: The aim of our study was to assess the diagnostic performance of commercially available AI ...

Volume Measurements for Surveillance after Endovascular Aneurysm Repair using Artificial Intelligence.

OBJECTIVE: Surveillance after endovascular aneurysm repair (EVAR) is suboptimal due to limited compl...

Exploring the Combination of Computer Vision and Surgical Neuroanatomy: A Workflow Involving Artificial Intelligence for the Identification of Skull Base Foramina.

BACKGROUND: The skull base is a complex region in neurosurgery, featuring numerous foramina. Accurat...

The Future of Sustainable Neurosurgery: Is a Moonshot Plan for Artificial Intelligence and Robot-Assisted Surgery Possible in Japan?

Japanese neurosurgery faces challenges such as a declining number of neurosurgeons and their concent...

Artificial Intelligence Prediction Model of Occurrence of Cerebral Vasospasms Based on Machine Learning.

BACKGROUND:  Symptomatic cerebral vasospasms are deleterious complication of the rupture of a cerebr...

Neurosurgery: AI-driven precision, robotics, and personalized care.

Reduced order modelling of intracranial aneurysm flow using proper orthogonal decomposition and neural networks.

Reduced order modelling (ROMs) methods, such as proper orthogonal decomposition (POD), systematicall...

Advancing aneurysm management: the potential of AI and machine learning in enhancing safety and predictive accuracy.

Cerebral aneurysm rupture, the predominant cause of non-traumatic subarachnoid hemorrhage, underscor...

Reproducibility and across-site transferability of an improved deep learning approach for aneurysm detection and segmentation in time-of-flight MR-angiograms.

This study aimed to (1) replicate a deep-learning-based model for cerebral aneurysm segmentation in ...

Predicting Intracranial Aneurysm Rupture: A Multifactor Analysis Combining Radscore, Morphology, and PHASES Parameters.

RATIONALE AND OBJECTIVES: We aimed at developing and validating a nomogram and machine learning (ML)...

Revolutionizing Aneurysm detection: The role of artificial intelligence in reducing rupture rates.

Cerebral aneurysms, affecting 2-5% of the global population, are often asymptomatic and commonly loc...

An ontology-based tool for modeling and documenting events in neurosurgery.

BACKGROUND: Intraoperative neurophysiological monitoring (IOM) plays a pivotal role in enhancing pat...

Deep Learning Based Automatic Segmentation of the Thoracic Aorta from Chest Computed Tomography in Healthy Korean Adults.

OBJECTIVE: Segmenting the aorta into zones based on anatomical landmarks is a current trend to bette...

Photonic deep residual time-delay reservoir computing.

Time-delay reservoir computing (TDRC) represents a simplified variant of recurrent neural networks, ...

Empowering Early Career Neurosurgeons in the Critical Appraisal of Artificial Intelligence and Machine Learning: The Design and Evaluation of a Pilot Course.

BACKGROUND: Artificial intelligence (AI) is expected to play a greater role in neurosurgery. There i...

Off-Label use of Woven EndoBridge device for intracranial brain aneurysm treatment: Modeling of occlusion outcome.

INTRODUCTION: The Woven EndoBridge (WEB) device is emerging as a novel therapy for intracranial aneu...

Deep learning for intracranial aneurysm segmentation using CT angiography.

This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4-...

A microdiscectomy surgical video annotation framework for supervised machine learning applications.

PURPOSE: Lumbar discectomy is among the most common spine procedures in the US, with 300,000 procedu...