Latest AI and machine learning research in neurosurgery for healthcare professionals.
Clinically manifested pneumonia associated with COVID-19 infection in cancer patients has been associated with worse prognosis. The prognostic significance of subclinical manifestations of pulmonary infiltration is poorly understood. To estimate the survival of cancer patients with signs of asymptomatic pneumonia detected by a multitarget artificial intelligence (AI) algorithm on chest computed to...
Intracranial aneurysms, with an annual incidence of 2%-3%, reflect a rare disease associated with significant mortality and morbidity risks when ruptured. Early detection, risk stratification of high-risk subgroups, and prediction of patient outcomes are important to treatment. Radiomics is an emerging field using the quantification of medical imaging to identify parameters beyond traditional radi...
Closed-loop electricalstimulation of brain structures is one of the most promising techniques to suppress epileptic seizures in drug-resistant refract...
PURPOSE: This study proposes a novel deep learning-based approach for aneurysm wall characteristics, including thin-walled (TW) and hyperplastic-remod...
Charge-domain compute-in-memory (CIM) SRAMs have recently become an enticing compromise between computing efficiency and accuracy to process sub-8b ...
Presented is a path towards a fast and robust adaptive anisotropic mesh generation method that is designed to help streamline the discretization of ...
Despite the success of CNN models on a variety of Image classification and segmentation tasks, their extensive computational and storage demands pos...
The project aims to develop differentially private deep learning models for image classification on CIFAR-10 datasets \cite{cifar10} and analyze the...
This paper presents a differentially private approach to Kaplan-Meier estimation that achieves accurate survival probability estimates while safegua...
Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneury...
Deep learning techniques have proven highly effective in image classification, but their deployment in resourceconstrained environments remains chal...
The aim of this study was to develop a machine-learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma....
Artificial intelligence (AI) is increasingly significant in neurosurgery, enhancing differential diagnosis, preoperative evaluation, and surgical prec...
Importance: Many individuals with drug-resistant epilepsy continue to have seizures after resective surgery. Accurate identification of focal brain ...
Stability in recurrent neural models poses a significant challenge, particularly in developing biologically plausible neurodynamical models that can...
PURPOSE: Recent artificial intelligence algorithms aided intraoperative decision-making via stimulated Raman histology (SRH) during craniotomy. This s...
We present an unsupervised deep learning method to perform flow denoising and super-resolution without high-resolution labels. We demonstrate the abil...
Background Deep learning (DL) could improve the labor-intensive, challenging processes of diagnosing cerebral aneurysms but requires large multicenter...