Neurosurgery

Fully automated detection and segmentation of intracranial aneurysms in subarachnoid hemorrhage on CTA using deep learning.

In aneurysmal subarachnoid hemorrhage (aSAH), accurate diagnosis of aneurysm is essential for subsequent treatment to prevent rebleeding. However, aneurysm detection proves to be challenging and time-consuming. The purpose of this study was to develop and evaluate a deep learning model (DLM) to automatically detect and segment aneurysms in patients with aSAH on computed tomography angiography. In ...

Fully automatic volume segmentation of infrarenal abdominal aortic aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation.

OBJECTIVE: Imaging software has become critical tools in the diagnosis and decision making for the treatment of abdominal aortic aneurysms (AAA). However, the interobserver reproducibility of the maximum cross-section diameter is poor. This study aimed to present and assess the quality of a new fully automated software (PRAEVAorta) that enables fast and robust detection of the aortic lumen and the...

Global adoption of robotic technology into neurosurgical practice and research.

Recent technological advancements have led to the development and implementation of robotic surgery in several specialties, including neurosurgery. Ou...

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interp...

Attitudes of the Surgical Team Toward Artificial Intelligence in Neurosurgery: International 2-Stage Cross-Sectional Survey.

BACKGROUND: Artificial intelligence (AI) has the potential to disrupt how we diagnose and treat patients. Previous work by our group has demonstrated ...

Predictive score for complete occlusion of intracranial aneurysms treated by flow-diverter stents using machine learning.

BACKGROUND: Complete occlusion of an intracranial aneurysm (IA) after the deployment of a flow-diverter stent is currently unpredictable. The aim of t...

Deep Learning for Detecting Cerebral Aneurysms with CT Angiography.

Background Cerebral aneurysm detection is a challenging task. Deep learning may become a supportive tool for more accurate interpretation. Purpose To ...

Automated Aneurysm Detection: Emerging from the Shallow End of the Deep Learning Pool.

Artificial Intelligence in Neurosurgery: a Systematic Review Using Topic Modeling. Part I: Major Research Areas.

UNLABELLED: In recent years, the number of scientific publications on artificial intelligence (AI), primarily on machine learning, with respect to neu...

Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier.

There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement ...

A generic physics-informed neural network-based constitutive model for soft biological tissues.

Constitutive modeling is a cornerstone for stress analysis of mechanical behaviors of biological soft tissues. Recently, it has been shown that machin...

Radiomic Model for Distinguishing Dissecting Aneurysms from Complicated Saccular Aneurysms on high-Resolution Magnetic Resonance Imaging.

OBJECTIVE: To build radiomic model in differentiating dissecting aneurysm (DA) from complicated saccular aneurysm (SA) based on high-resolution magnet...

Deep learning-based lumbosacral reconstruction for difficulty prediction of percutaneous endoscopic transforaminal discectomy at L5/S1 level: A retrospective cohort study.

BACKGROUND: Deep learning has been validated as a promising technique for automatic segmentation and rapid three-dimensional (3D) reconstruction of lu...

Prediction, pattern recognition and modelling of complications post-endovascular infra renal aneurysm repair by artificial intelligence.

OBJECTIVES: The study evaluates the plausibility and applicability of prediction, pattern recognition and modelling of complications post-endovascular...

Machine learning in neurosurgery: a global survey.

BACKGROUND: Recent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including ne...

The Future of Skull Base Surgery: A View Through Tinted Glasses.

In the present report, we have broadly outlined the potential advances in the field of skull base surgery, which might occur within the next 20 years ...

High-quality photoacoustic image reconstruction based on deep convolutional neural network: towards intra-operative photoacoustic imaging.

The use of intra-operative imaging system as an intervention solution to provide more accurate localization of complicated structures has become a nec...

A pilot study using a machine-learning approach of morphological and hemodynamic parameters for predicting aneurysms enhancement.

PURPOSE: The development of straightforward classification methods is needed to identify unstable aneurysms and rupture risk for clinical use. In this...

A deep learning algorithm may automate intracranial aneurysm detection on MR angiography with high diagnostic performance.

OBJECTIVES: To develop a deep learning algorithm for automated detection and localization of intracranial aneurysms on time-of-flight MR angiography a...

Fully automated intracranial aneurysm detection and segmentation from digital subtraction angiography series using an end-to-end spatiotemporal deep neural network.

BACKGROUND: Intracranial aneurysms (IAs) are common in the population and may cause death.