Neurosurgery

Adherence to SVS Abdominal Aortic Aneurysm Guidelines Among Pati ents Detected by AI-Based Algorithm.

OBJECTIVES: This study evaluates adherence to the latest Society for Vascular Surgery (SVS) guidelines on imaging surveillance, physician evaluation, and surgical intervention for abdominal aortic aneurysm (AAA).

Synthetic Lung Ultrasound Data Generation Using Autoencoder With Generative Adversarial Network.

Class imbalance is a significant challenge in medical image analysis, particularly in lung ultrasound (LUS), where severe patterns are often underrepresented. Traditional oversampling techniques, which simply duplicate original data, have limited effectiveness in addressing this issue. To overcome these limitations, this study introduces a novel supervised autoencoder generative adversarial networ...

PMMNet: A Dual Branch Fusion Network of Point Cloud and Multi-View for Intracranial Aneurysm Classification and Segmentation.

Intracranial aneurysm (IA) is a vascular disease of the brain arteries caused by pathological vascular dilation, which can result in subarachnoid hemo...

The role of artificial intelligence in diagnostic neurosurgery: a systematic review.

BACKGROUND: Artificial intelligence (AI) is increasingly applied in diagnostic neurosurgery, enhancing precision and decision-making in neuro-oncology...

Predicting outcomes following open abdominal aortic aneurysm repair using machine learning.

Patients undergoing open surgical repair of abdominal aortic aneurysm (AAA) have a high risk of post-operative complications. However, there are no wi...

Identification of immune-related biomarkers and immune infiltrations of intracranial aneurysm with subarachnoid hemorrhage by machine-learning strategies.

Background: Subarachnoid hemorrhage (SAH) risk increases with intracranial aneurysms (IA), but their relationship remains unclear. Methods: We explore...

AtlasGPT: a language model grounded in neurosurgery with domain-specific data and document retrieval.

OBJECTIVE: Large language models (LLMs) have shown promising performance on medical licensing examinations, but their ability to excel in subspecialty...

Identification of key therapeutic targets in nicotine-induced intracranial aneurysm through integrated bioinformatics and machine learning approaches.

BACKGROUND: Intracranial aneurysm (IA) is a critical cerebrovascular condition, and nicotine exposure is a known risk factor. This study delves into t...

Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in intracranial aneurysma.

BACKGROUND: Increasing evidence indicates a connection between intracranial aneurysm (intracranial aneurysm, IA) and autoimmune diseases. However, the...

Investigating long-term risk of aortic aneurysm and dissection from fluoroquinolones and the key contributing factors using machine learning methods.

The connection between fluoroquinolones and severe heart conditions, such as aortic aneurysm (AA) and aortic dissection (AD), has been acknowledged, b...

An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment.

Chronic disease (CD) like diabetes and stroke impacts global healthcare extensively, and continuous monitoring and early detection are necessary for e...

Multimodal imaging platform for enhanced tumor resection in neurosurgery: integrating hyperspectral and pCLE technologies.

PURPOSE: This work presents a novel multimodal imaging platform that integrates hyperspectral imaging (HSI) and probe-based confocal laser endomicrosc...

Accuracy and quality of ChatGPT-4o and Google Gemini performance on image-based neurosurgery board questions.

Large-language models (LLMs) have shown the capability to effectively answer medical board examination questions. However, their ability to answer ima...

Letters to the editor generated by AI in neuroscience: The role of neuroethics.

The letter to the editor (LTE) is a correspondence forum that allows a journal's readers to comment on published research and also publish data and ar...

Artificial intelligence in academic writing: Enhancing or replacing human expertise?

This letter discusses findings from a recent study comparing AI-generated and humanwritten neurosurgery articles. The study reveals that AI-generated ...

Identification and Experimental Validation of NETosis-Mediated Abdominal Aortic Aneurysm Gene Signature Using Multi-omics, Machine Learning, and Mendelian Randomization.

Abdominal aortic aneurysm (AAA) is a life-threatening disorder with limited therapeutic options. Neutrophil extracellular traps (NETs) are formed by a...

Artificial Intelligence in Vascular Diseases: From Clinical Practice to Medical Research and Education.

Artificial Intelligence (AI) has brought new opportunities in medicine, with a great potential to improve care provided to patients. Given the technic...

A computed tomography angiography-based radiomics model for prognostic prediction of endovascular abdominal aortic repair.

OBJECTIVE: This study aims to develop a radiomics machine learning (ML) model that uses preoperative computed tomography angiography (CTA) data to pre...

Diagnostic Accuracy of a Deep Learning Algorithm for Detecting Unruptured Intracranial Aneurysms in Magnetic Resonance Angiography: A Multicenter Pivotal Trial.

BACKGROUND: Intracranial aneurysm rupture is associated with high mortality and disability rates. Early detection is crucial, but increasing diagnosti...

Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.

BACKGROUND: Thoracic endovascular aortic repair (TEVAR) and complex endovascular aneurysm repair (EVAR) are complex procedures that carry a significan...