Computational neurosurgery is a novel and disruptive field where artificial intelligence and computational modeling are used to improve the diagnosis, treatment, and prognosis of patients affected by diseases of neurosurgical relevance. The field aims to bring new knowledge to clinical neurosciences and inform on the profound questions related to the human brain by applying augmented intelligence, where the power of artificial intelligence and computational inference can enhance human expertise. This transformative field requires the articulation of ethical considerations that will enable scientists, engineers, and clinical neuroscientists, including neurosurgeons, to ensure that the use of such a powerful application is conducted based on the highest moral and ethical standards with a patient-centric approach to predict and prevent mistakes. This declaration is a first attempt to draw a roadmap to guide the application of practical or applied ethics to computational neurosurgery. It is intended for the use of practitioners, ethicists, and scientists using artificial intelligence to understand and treat all the pathophysiological conditions related to the human brain.
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http://dx.doi.org/10.1007/978-3-031-64892-2_2 | DOI Listing |
Anat Sci Int
December 2024
Department of Anatomy, School of Medicine, Faculty of Health Sciences, National and Kapodistrian University of Athens, 75 Mikras Asias Str., Goudi, 11527, Athens, Greece.
The cerebral arterial circle morphologic variability has been extensively studied. The posterior cerebral artery (PCA) variants are rarely identified, except from the first segment (P1) hypoplasia or absence. Due to its unique morphology, the computed tomography angiography (CTA) of a 34-year-old female patient was further investigated.
View Article and Find Full Text PDFChilds Nerv Syst
December 2024
Department of Neurosurgery, Osaka Women's and Children's Hospital, Izumi, Osaka, 594-1101, Japan.
Purpose: This study presents a MATrix LABoratory (MATLAB)-based methodology for calculating intracranial volumes from head computed tomography (CT) data and compares it with established methods.
Methods: Regions of interest (ROI) were manually segmented on CT images using a stylus pen, facilitated by mirroring a computer desktop onto a tablet. The volumetric process involved three main steps: (1) calculating the volume of a single voxel, (2) counting the total number of voxels within the segmented ROI, and (3) multiplying this voxel count by the single-voxel volume.
J Neuroendovasc Ther
October 2024
Division of Cerebrovascular Therapy, Kobe City Medical Center General Hospital, Kobe, Hyogo, Japan.
Objective: This study aimed to simulate patient transportation to a mechanical thrombectomy (MT)-capable hospital within 60 minutes, taking into account patient volume (demand side of healthcare) and hospital capacity to accept patients (supply side of healthcare).
Methods: Simulations were conducted in Hyogo Prefecture, Japan. The estimates of the annual number of patients with stroke eligible for MT in 2020 were based on the incidence of stroke by age group and the percentage of patients with stroke indicated for MT in existing publications.
Algorithms
December 2023
Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternative to signal inspection, but subtle variations may escape the human eye. Here, we propose a deep learning-based metric of visual complexity to interpret TF images extracted from iEEG data and aim to assess its ability to identify the EZ in the brain.
View Article and Find Full Text PDFCurr Med Imaging
December 2024
Department of Vascular Neurosurgery, National Institute of Neurology and Neurosurgery "Manuel Velasco Suárez", 14267, Mexico City, Mexico.
Background: Castleman disease (CD) is a rare lymphoproliferative disorder, with intracranial involvement being exceedingly rare. Unicentric Castleman disease (UCD) is typically benign and localized, but its presentation can mimic other intracranial pathologies, complicating diagnosis.
Case Description: We reported a 52-year-old woman who presented with progressive headaches and language disturbances.
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