Latest AI and machine learning research in neurosurgery for healthcare professionals.
Cerebral aneurysm is a silent yet prevalent condition that affects a substantial portion of the glob...
Surgical success hinges on two core factors: technical execution and cognitive planning. While the f...
Rare or unexpected postoperative neurosurgical complications pose a challenge due to clinical variab...
Precise aneurysm volume measurement offers a transformative edge for risk assessment and treatment p...
Proteomic techniques now measure thousands of proteins circulating in blood at population scale, dri...
Traditional medical board examinations present clinical information in static vignettes with multipl...
Real-time brain monitoring for neurosurgery and neuroscience research of natural behaviors demands p...
Automated interpretation of resting-state fMRI (rs-fMRI) for epilepsy diagnosis remains a challenge....
Clinical guidelines recommend using an absolute ascending aortic diameter (AAD) cutoff of 4.5 cm for...
Pediatric brain tumors are the leading cause of cancer death in children, with surgical resection cr...
To evaluate how reader experience influences the diagnostic benefit from LLM assistance in brain MRI...
MRI-derived organ and tissue volumes are powerful endophenotypes for studying complex disease, but t...
Clinically manifested pneumonia associated with COVID-19 infection in cancer patients has been assoc...
Intracranial aneurysms, with an annual incidence of 2%-3%, reflect a rare disease associated with si...
Closed-loop electricalstimulation of brain structures is one of the most promising techniques to sup...
PURPOSE: This study proposes a novel deep learning-based approach for aneurysm wall characteristics,...
Charge-domain compute-in-memory (CIM) SRAMs have recently become an enticing compromise between co...
Presented is a path towards a fast and robust adaptive anisotropic mesh generation method that is ...
Despite the success of CNN models on a variety of Image classification and segmentation tasks, the...
The project aims to develop differentially private deep learning models for image classification o...
This paper presents a differentially private approach to Kaplan-Meier estimation that achieves acc...