Neurosurgery

Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection

Introduction: Deep learning (DL) models can help detect intracranial aneurysms on CTA, but high false positive (FP) rates remain a barrier to clinical translation, despite improvement in model architectures and strategies like detection threshold tuning. We employed an automated, anatomy-based, heuristic-learning hybrid artery-vein segmentation post-processing method to further reduce FPs. Metho...

Introduction. Artificial intelligence in neurosurgery: transforming a data-intensive specialty.

Phenotype-driven risk stratification of cerebral aneurysms using Shapley Additive Explanations-based supervised clustering: a novel approach to rupture prediction.

OBJECTIVE: The aim of this study was to address the limitations of traditional aneurysm risk scoring systems and computational fluid dynamics (CFD) an...

Multitask Deep Learning for Automated Detection of Endoleak at Digital Subtraction Angiography during Endovascular Aneurysm Repair.

Purpose To develop and evaluate a novel multitask deep learning framework for automated detection and localization of endoleaks at aortic digital subt...

Methodological Challenges in Deep Learning-Based Detection of Intracranial Aneurysms: A Scoping Review.

Artificial intelligence (AI), particularly deep learning, has demonstrated high diagnostic performance in detecting intracranial aneurysms on computed...

Critical Anatomy-Preserving & Terrain-Augmenting Navigation (CAPTAiN): Application to Laminectomy Surgical Education

Surgical training remains a crucial milestone in modern medicine, with procedures such as laminectomy exemplifying the high risks involved. Laminect...

RaRa Clipper: A Clipper for Gaussian Splatting Based on Ray Tracer and Rasterizer

With the advancement of Gaussian Splatting techniques, a growing number of datasets based on this representation have been developed. However, perfo...

Graph-Convolution-Beta-VAE for Synthetic Abdominal Aorta Aneurysm Generation

Synthetic data generation plays a crucial role in medical research by mitigating privacy concerns and enabling large-scale patient data analysis. Th...

First-in-human, real-time artificial intelligence assisted cerebral aneurysm coiling: a preliminary experience.

BACKGROUND: Neuroendovascular procedures require careful and simultaneous attention to multiple devices on multiple screens. Overlooking unintended de...

DWI-based deep learning radiomics nomogram for predicting the impaired quality of life in patients with unruptured intracranial aneurysm developing new iatrogenic cerebral infarcts following stent placement: a multicenter cohort study.

This study developed a DWI-based radiomics nomogram to predict impaired health-related quality of life (HRQOL) in patients with unruptured intracrania...

Q-SAM2: Accurate Quantization for Segment Anything Model 2

The Segment Anything Model 2 (SAM2) has gained significant attention as a foundational approach for promptable image and video segmentation. However...

Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces

Abdominal aortic aneurysms (AAAs) are progressive focal dilatations of the abdominal aorta. AAAs may rupture, with a survival rate of only 20\%. Cur...

GeoClip: Geometry-Aware Clipping for Differentially Private SGD

Differentially private stochastic gradient descent (DP-SGD) is the most widely used method for training machine learning models with provable privac...

Generalized Gradient Norm Clipping & Non-Euclidean $(L_0,L_1)$-Smoothness

This work introduces a hybrid non-Euclidean optimization method which generalizes gradient norm clipping by combining steepest descent and condition...

Advancing Intracranial Aneurysm Detection: A Comprehensive Systematic Review and Meta-analysis of Deep Learning Models Performance, Clinical Integration, and Future Directions.

BACKGROUND: Cerebral aneurysms pose a significant risk to patient safety, particularly when ruptured, emphasizing the need for early detection and acc...

Evaluating the performance and fragility of large language models on the self-assessment for neurological surgeons

The Congress of Neurological Surgeons Self-Assessment for Neurological Surgeons (CNS-SANS) questions are widely used by neurosurgical residents to p...

Artificial intelligence in neurosurgery: a systematic review of applications, model comparisons, and ethical implications.

BACKGROUND: Artificial Intelligence (AI) has emerged as a transformative tool in medicine, particularly addressing neurosurgical challenges such as co...

In Search of Adam's Secret Sauce

Understanding the remarkable efficacy of Adam when training transformer-based language models has become a central research topic within the optimiz...

Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images.

BACKGROUND: Pineal region tumors (PRTs) are rare but deep-seated brain tumors, and complete surgical resection is crucial for effective tumor treatmen...

Knoll: Creating a Knowledge Ecosystem for Large Language Models

Large language models are designed to encode general purpose knowledge about the world from Internet data. Yet, a wealth of information falls outsid...