Neurosurgery

Comparison of Different Machine Learning Methods in Prediction of Long-Term Survival After Endovascular Aneurysm Repair.

PurposeLong-term survival after endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm remains a clinical concern, particularly in elderly patients with comorbidities. This study aimed to compare different machine learning (ML) models that capture complex, nonlinear relationships among clinical variables to predict 5-year all-cause mortality following EVAR.MethodsWe retrospectively anal...

Application of large language and artificial intelligence modeling in the prediction of peer-review outcomes.

OBJECTIVE: The rapid development of artificial intelligence (AI) presents an opportunity to streamline the peer-review process and provide key information to guide academic journals, editorial staff, and reviewers, as well as authors. This study aimed to fine-tune several standard large language and transformer models (LLMs) on the basis of the text of peer-reviewer comments and editorial outcome ...

Interpretable classification of endomicroscopic brain data via saliency consistent contrastive learning.

In neurosurgery, accurate brain tissue characterization via probe-based Confocal Laser Endomicroscopy (pCLE) has become popular for guiding surgical d...

Targeting High-Risk Populations for Abdominal Aortic Aneurysm Screening: A Sex-Stratified Analysis in an Ethnicity Diverse Cohort Incorporating Machine Learning Tools.

BACKGROUND: Abdominal aortic aneurysm (AAA) is usually asymptomatic, but rupture carries up to 90% mortality. Ultrasound screening reduces rupture-rel...

Progress in constitutive modeling of arterial wall tissue mechanics: from theoretical frameworks to clinical application.

Cardiovascular disease (CVD) is a leading cause of mortality worldwide, and the mechanical behavior of arterial wall tissue (AWT) is central to its in...

Accuracy of fully automated iliac artery tortuosity measurements in patients with abdominal aortic aneurysm using deep learning.

OBJECTIVE: To externally validate the accuracy of deep learning-based iliac artery tortuosity assessment (PRAEVAorta 2, Bordeaux, France) in computed ...

Align then clip: Refining graph for face clustering.

Face clustering, a critical task for annotating large-scale unlabeled face recognition datasets, aims to group facial images of the same identity whil...

Performance of deep-learning reconstruction combined with metal artifact reduction algorithm for dual-energy computed tomography angiography in intracranial aneurysm coil embolization.

PURPOSES: To evaluate the diagnostic confidence in cerebral aneurysm embolization coil follow-up using the deep learning image reconstruction (DLIR) b...

[Expert consensus on neurosurgical robot-assisted deep brain stimulation surgery (2026 edition)].

Robot-assisted deep brain stimulation (DBS) surgical systems in neurosurgery have demonstrated significant advantages in enhancing operative precision...

Development and Validation of an Interpretable Machine Learning Model for Predicting Early Fusion After Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: Retrospective case-control study. OBJECTIVES: This study aimed to develop and preliminarily validate a machine learning (ML) model for p...

Systematic machine learning approach for cerebral aneurysm feature selection and rupture status classification.

BACKGROUND: Current methods of intracranial aneurysm rupture risk assessment in the clinical setting depend on user measurements of morphological fact...

Evaluating the Performance and Fragility of Large Language Models on the Self-Assessment for Neurological Surgeons.

BACKGROUND AND OBJECTIVES: The Congress of Neurological Surgeons Self-Assessment for Neurological Surgeons questions are widely used by neurosurgical ...

Risk Models for Adverse Events in Microsurgery for Intracranial Unruptured Aneurysms.

BACKGROUND AND OBJECTIVES: Preventive treatment of unruptured intracranial aneurysms (UIAs) requires assessment of treatment risks vs expected benefit...

GN-Net: A Geometric and Neighborhood-Aware Network for Predicting Intracranial Aneurysm Rupture Risk and Assisting Clinical Decision-Making.

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 fatal consequences in >80% of cases...

Three-dimensional surface scanning for registration in stereotactic neurosurgery: a cadaveric feasibility study.

OBJECTIVE: Achieving submillimetric accuracy in stereotactic neurosurgery remains critical for safely targeting deep brain structures. Current workflo...

Adaptive differential privacy mechanism for enhanced deep learning model utility and privacy.

The potential leakage of training data privacy in deep learning has been a topic of concern for researchers and the public. To provide formal and rigo...

Explainable Convolutional Neural Networks for the identification of the Ampullariidae genus.

Invasive freshwater snails of the family Ampullariidae present significant threats to agriculture, biodiversity, and public health. Recent advances in...

Unsupervised machine learning for identifying morphological phenotypes in abdominal aortic aneurysms using fully automated volume-segmented imaging: a multicentre cohort study.

AIMS: Thrombo- and microembolic complications following abdominal aortic aneurysm (AAA) repair are hypothesized to be associated with wall thrombus bu...

An Internet of Things operating room platform for neurosurgery.

OBJECTIVE: The operating room (OR) is a data-rich environment and largely follows closed-door policies for health data security and privacy. To overco...