Latest AI and machine learning research in neurosurgery for healthcare professionals.
Despite the plethora of AI-based algorithms developed for anomaly detection in radiology, subsequent integration into clinical setting is rarely evaluated. In this work, we assess the applicability and utility of an AI-based model for brain aneurysm detection comparing the performance of two readers with different levels of experience (2 and 13 years). We aim to answer the following questions: 1...
Due to the limited accuracy of 4D Magnetic Resonance Imaging (MRI) in identifying hemodynamics in cardiovascular diseases, the challenges in obtaining patient-specific flow boundary conditions, and the computationally demanding and time-consuming nature of Computational Fluid Dynamics (CFD) simulations, it is crucial to explore new data assimilation algorithms that offer possible alternatives to...
Vision-Language Models (VLMs) leverage aligned visual encoders to transform images into visual tokens, allowing them to be processed similarly to te...
Abdominal aortic aneurysms (AAAs) are localized dilatations of the abdominal aorta that can lead to life-threatening rupture if left untreated. AAAs...
Introduction: Timely care in a specialised neuro-intensive therapy unit (ITU) reduces mortality and hospital stays, with planned admissions being sa...
Personalized federated learning is extensively utilized in scenarios characterized by data heterogeneity, facilitating more efficient and automated ...
Objective: ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local ...
Objective: we propose a procedure for calibrating 4 parameters governing the mechanical boundary conditions (BCs) of a thoracic aorta (TA) model der...
Reinforcement learning (RL)-based fine-tuning has emerged as a powerful approach for aligning diffusion models with black-box objectives. Proximal p...
Background: With the increasing availability of healthcare data, predictive modeling finds many applications in the biomedical domain, such as the e...
Data augmentation effectively addresses the imbalanced-small sample data (ISSD) problem in hyperspectral image classification (HSIC). While most met...
Leading vision-language models (VLMs) are trained on general Internet content, overlooking scientific journals' rich, domain-specific knowledge. Tra...
This paper comprehensively evaluates several recently proposed optimizers for 4-bit training, revealing that low-bit precision amplifies sensitivity...
Introduction: Computer vision (CV) has had a transformative impact in biomedical fields such as radiology, dermatology, and pathology. Its real-worl...
Natural Language Processing (NLP) and Voice Recognition agents are rapidly evolving healthcare by enabling efficient, accessible, and professional p...
Abdominal aortic aneurysm (AAA) is a life-threatening condition involving the permanent dilation of the aorta, often detected incidentally through i...
Federated learning (FL) is a popular paradigm for collaborative training which avoids direct data exposure between clients. However, data privacy is...
BACKGROUND: The efficacy of laminectomy procedures is contingent on the method of resection. The objective of this study was to investigate the impact...
BACKGROUND: Neurosurgery demands high precision, and robotic-assisted systems are increasingly employed to enhance surgical outcomes. This study focus...
BACKGROUND: The relentless integration of Artificial Intelligence (AI) into neurosurgery necessitates a meticulous exploration of the associated ethic...