Neurosurgery

Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review

Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupt...

Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task

Deep learning techniques have proven highly effective in image classification, but their deploymen...

Development of machine learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma.

The aim of this study was to develop a machine-learning prediction model for AKI after craniotomy an...

Letter to Editor Regarding "Use of Artificial Intelligence Software to Detect Intracranial Aneurysms: A Comprehensive Stroke Center Experience".

Artificial intelligence (AI) is increasingly significant in neurosurgery, enhancing differential dia...

Seizure freedom after surgical resection of diffusion-weighted MRI abnormalities

Importance: Many individuals with drug-resistant epilepsy continue to have seizures after resectiv...

Unconditional stability of a recurrent neural circuit implementing divisive normalization

Stability in recurrent neural models poses a significant challenge, particularly in developing bio...

Streamlined Intraoperative Brain Tumor Classification and Molecular Subtyping in Stereotactic Biopsies Using Stimulated Raman Histology and Deep Learning.

PURPOSE: Recent artificial intelligence algorithms aided intraoperative decision-making via stimulat...

Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning.

We present an unsupervised deep learning method to perform flow denoising and super-resolution witho...

Letter to the Editor Regarding "Medical Ethics and Artificial Intelligence in Neurosurgery-How Should We Prepare?".

In Reply to the Letter to the Editor Regarding: "Medical Ethics and Artificial Intelligence in Neurosurgery-How Should We Prepare?".

Knowledge-Augmented Deep Learning for Segmenting and Detecting Cerebral Aneurysms With CT Angiography: A Multicenter Study.

Background Deep learning (DL) could improve the labor-intensive, challenging processes of diagnosing...

An efficient pipeline to compute patient-specific cerebral aneurysm wall tension

Cerebral aneurysm rupture, leading to subarachnoid hemorrhage with a high mortality rate, dispropo...

Abdominal aortic aneurysm wall stress: A 7-line code in MATLAB and a one-click software application

An abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by the irreversib...

Fuzzy-Label Weighted Deep Learning Classification for CT Image Quality Evaluation.

This paper proposes a fuzzy-label weighted deep learning-based image classification approach for ass...

Optimizing Coil Selection for Cerebral Aneurysm Treatment Using PyRadiomics and Machine Learning Models.

This study presents an innovative method to increase the accuracy of coil selection for treating cer...

Revolutionizing aortic aneurysm assessment with artificial intelligence.

Letter to the Editor Regarding "Exploring the Landscape of Machine Learning Applications in Neurosurgery: A Bibliometric Analysis and Narrative Review of Trends and Future Directions".

A deep-learning model for intracranial aneurysm detection on CT angiography images in China: a stepwise, multicentre, early-stage clinical validation study.

BACKGROUND: Artificial intelligence (AI) models in real-world implementation are scarce. Our study a...

Letter to the Editor Regarding "Targeting the Future: Developing a Training Curriculum for Robotic Assisted Neurosurgery".

AI-powered Hyperrealism: Next Step in Cinematic Rendering?

Background Recent advancements in artificial intelligence (AI)-powered image generation present oppo...

Computational Neurosurgery: Foundation.

Computational neurosurgery is a novel translational field where computational modeling and artificia...