Neurosurgery

Accuracy and Readability of Generative Artificial Intelligence for Vascular Surgery Patients: A Specialist Based Evaluation Highlighting the Current Landscape of Safety Risks and Accessibility Gaps.

OBJECTIVE: Patients frequently seek health information and medical advice from chatbots instead of consulting their physicians or referring to credible patient education resources provided by medical societies. A study to evaluate the quality, readability, and clinical appropriateness of ChatGPT generated answers to common vascular surgery questions was designed. METHODS: Sixteen questions, writte...

Machine Learning Based on Multiparametric Features from Dual-Layer Detector Spectral CT for Identifying Ulcer-Like Projection in Aortic Intramural Hematoma.

Aortic intramural hematoma (IMH) is a high-risk subtype of acute aortic syndrome (AAS). The presence of ulcer-like projection (ULP) significantly increases the risk of progression to dissection, aneurysm, or rupture. However, conventional computed tomography angiography (CTA) has limited reliability in differentiating small ULP from the surrounding high-density hematoma or coexisting atherosclerot...

An interpretable machine learning approach to predict left ventricular aneurysm formation following primary PCI in STEMI: A retrospective study with independent external validation.

BACKGROUND: Left ventricular aneurysm (LVA) remains a clinically important structural complication after primary percutaneous coronary intervention (p...

Automated Generation and Human Evaluation of Neurosurgical Board Examination Self-Assessment Questions.

BACKGROUND AND OBJECTIVES: Multiple-choice questions are the primary assessment format for neurosurgical board certification. Creating high-quality ex...

Artificial Intelligence in Neurosurgery: From Technical Metrics to Deployable Clinical Evidence.

Letter: Artificial Intelligence and the Jagged Frontier of Clinical Reasoning: Lessons from Neurosurgery.

[Overview and conclusions of the STROKE data platform of the National Laboratory for Translational Neuroscience and the associated clinical studies].

Acute ischemic and hemorrhagic stroke are among the leading causes of mortality and long-term disability worldwide. In addition to the results of rand...

Data-centric physics-inspired deep learning framework for saturation artifact removal in optical coherence tomography.

In Fourier-domain optical coherence tomography (FD-OCT) imaging, saturation of the data acquisition chain leads to loss of information by clipping the...

Patient and physician perspectives on large language model generated responses about brain aneurysm.

PURPOSE: Large language models (LLMs) are becoming increasingly popular in medicine and neurosurgery. Because LLMs are not trained in specific subspec...

Optimizing the mathematical model and technical standards for unilateral biportal endoscopic spinal surgery through machine learning-based video analysis.

OBJECTIVE: To enhance the efficacy of the unilateral biportal endoscopic spinal procedure, the spinal triangle concept is proposed, integrating a digi...

Intraoperative technological advances and new frontiers in precision glioma surgery.

BACKGROUND: Diffuse gliomas remain among the most surgically challenging tumors, characterized by their infiltrative nature, proximity to eloquent bra...

Enhancing decision-making in surgery for a large temporocorneal meningioma through an explainable human-AI collaboration: a case report.

BACKGROUND: Meningiomas, particularly large temporocorneal meningiomas, pose significant surgical challenges due to their proximity to critical brain ...

Vibe Coding in Neurosurgery: Bridging the Gap Between Clinical Needs and Digital Innovation-A Narrative Review and Practical Perspective.

BackgroundArtificial intelligence (AI) and machine learning are transforming neurosurgical research and practice, yet the programming barrier has excl...

Adaptive learning rate methodologies and clipping mechanisms based upon gradient entropy.

Serving as a pivotal parameter during deep learning training processes, learning rate plays a crucial role regarding training efficiency. Traditional ...

End-to-End Autonomous Quantification of Brain Aneurysm and Parent Artery Morphology at CT Angiography.

Purpose To develop and validate an end-to-end autonomous platform for the quantification and visualization of brain aneurysm and parent artery morphol...

A single-source preoperative prediction model for recurrent lumbar disc herniation after primary discectomy: Development and internal validation in the SPORT registry.

BACKGROUND: Recurrent lumbar disc herniation (rLDH) follows primary discectomy in 5% to 25% of patients. Existing models often embed postoperative var...

Morphological parameters contributing to aneurysm rupture: Identifying the rogue from the mugshot using different statistical methods.

INTRODUCTION: Current scores that discriminate rupture status/rupture-associated morphology of aneurysm rupture rely on size being >7 mm, ignoring imp...

Noninvasive decoding of typed sentences from human brain activity.

Modern neuroprostheses can now restore communication in patients who have lost the ability to speak or move. However, implanting these invasive device...

The Unseen Aneurysm: Can Artificial Intelligence Bridge the Diagnostic Gap in Angiogram-Negative Subarachnoid Hemorrhage?

Evaluating expert decision-pattern alignment in endovascular planning for intracranial aneurysms using a multimodal large language model.

BACKGROUND: Endovascular treatment planning for intracranial aneurysms requires integrating vascular geometry, branch preservation, device feasibility...