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Development and validation of a clinical features-based nomogram for predicting neonatal cerebral microbleeds.
Quant Imaging Med Surg
January 2025
Department of Radiology, The First Hospital of Tsinghua University, Beijing, China.
Background: Neonatal cerebral microbleeds (CMBs) occur infrequently, and during the initial phase, they often present without noticeable clinical symptoms, which can result in delays in both diagnosis and treatment. There has been relatively little research conducted on neonatal CMBs, with even less focus on their related risk factors. However, identifying risk factors and proactively preventing microbleeds is particularly crucial for effective treatment.
View Article and Find Full Text PDFReal-world evaluation of the accuracy of the Viz.AI automated intracranial hemorrhage volume calculation tool.
J Neurointerv Surg
January 2025
Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Background: Appropriate management of spontaneous intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) requires rapid, accurate volume estimation. Viz.AI has developed an artificial intelligence (AI)-powered ICH calculation tool that may improve existing methods.
View Article and Find Full Text PDFPredicting the risk of hematoma expansion in acute intracerebral hemorrhage: the GIVE score.
BMC Neurol
January 2025
Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, NO1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
Background: Numerous noncontrast computed tomography (NCCT) markers have been reported and validated as effective predictors of hematoma expansion (HE). Our objective was to develop and validate a score based on NCCT markers and clinical characteristics to predict risk of HE in acute intracerebral hemorrhage (ICH) patients.
Methods: We prospectively collected spontaneous ICH patients at the First Affiliated Hospital of Chongqing Medical University to form the development cohort (n = 395) and at the Third Affiliated Hospital of Chongqing Medical University to establish the validation cohort (n = 139).
Classification Prediction of Hydrocephalus After Intercerebral Haemorrhage Based on Machine Learning Approach.
Neuroinformatics
January 2025
Department of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, 410114, China.
In order to construct a clinical classification prediction model for hydrocephalus after intercerebral haemorrhage(ICH) to guide clinical treatment decisions, this paper retrospectively analyses the clinical data of 844 cases of ICH and hydrocephalus inpatients admitted to Yueyang People's Hospital from May 2019 to October 2022, of which 95 cases of hydrocephalus occurred after ICH and no hydrocephalus in 749 cases. The following indicators were compared between the two groups of patients: gender, age, Glasgow Coma Scale(GCS)score, whether the amount of bleeding was greater than 30 ml, whether it broke into the ventricle or not, modified Graeb score(MGS), modified Rankin Scale (MRS) score, whether surgery was performed or not, red blood cells, white blood cells, and platelets. After variable screening, the following six variables were selected: GCS score, MGS, MRS score, whether the bleeding volume was greater than 30 ml, whether it broke into the ventricle or not, and whether surgery was performed or not were modelled and analysed using logistic regression model and support vector machine model in machine learning.
View Article and Find Full Text PDFPrenatally diagnosed intracranial hemorrhage in the fetus is associated with a wide range of neonatal disorders, from completely uncomplicated physiological development to severe neurological impairment or death. The incidence is 0.6-1/1,000 births.
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