AIMC Topic: Cerebral Hemorrhage

Clear Filters Showing 11 to 20 of 170 articles

Automated Quantification of Cerebral Microbleeds in SWI: Association with Vascular Risk Factors, White Matter Hyperintensity Burden, and Cognitive Function.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: The amount and distribution of cerebral microbleeds (CMB) are important risk factors for cognitive impairment. Our objective was to train and validate a deep learning (DL)-based segmentation model for cerebral microbleeds (CMB...

Risk Factors and Outcomes of Hemorrhagic Transformation in Acute Ischemic Stroke Following Thrombolysis: Analysis of a Single-Center Experience and Review of the Literature.

Medicina (Kaunas, Lithuania)
: This is a retrospective study conducted at the Clinical County Hospital of Craiova, Romania, providing valuable insights into hemorrhagic transformation (HT) in thrombolyzed patients with acute ischemic stroke (AIS). Hemorrhagic complications remai...

Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings.

BMC medical informatics and decision making
BACKGROUND: Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantly contributes to high mortality rates. This study aims to construct a mortality prediction model for patients with SICH using four various artificial ...

Predicting hemorrhagic transformation in acute ischemic stroke: a systematic review, meta-analysis, and methodological quality assessment of CT/MRI-based deep learning and radiomics models.

Emergency radiology
Acute ischemic stroke (AIS) is a major cause of mortality and morbidity, with hemorrhagic transformation (HT) as a severe complication. Accurate prediction of HT is essential for optimizing treatment strategies. This review assesses the accuracy and ...

Early prediction of intraventricular hemorrhage in very low birth weight infants using deep neural networks with attention in low-resource settings.

Scientific reports
Early prediction of intraventricular hemorrhage (IVH) in very low-birthweight infants (VLBWIs) remains challenging because of multifactorial risk factors. IVH often occurs within a few hours after birth, yet its onset cannot be reliably predicted usi...

Automatic cerebral microbleeds detection from MR images via multi-channel and multi-scale CNNs.

Computers in biology and medicine
BACKGROUND: Computer-aided detection (CAD) systems have been widely used to assist medical professionals in interpreting medical images, aiding in the detection of potential diseases. Despite their usefulness, CAD systems cannot yet fully replace doc...

Death risk prediction model for patients with non-traumatic intracerebral hemorrhage.

BMC medical informatics and decision making
BACKGROUND: This study aimed to assess the risk of death from non-traumatic intracerebral hemorrhage (ICH) using a machine learning model.

Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study.

European radiology experimental
BACKGROUND: Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute ischemic stroke (AIS). We aimed to develop and validate a model for predicting HT and its subtypes with poor prognosis-parenchymal hemorrhage (PH), i...

Classification Prediction of Hydrocephalus After Intercerebral Haemorrhage Based on Machine Learning Approach.

Neuroinformatics
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...

ICH-PRNet: a cross-modal intracerebral haemorrhage prognostic prediction method using joint-attention interaction mechanism.

Neural networks : the official journal of the International Neural Network Society
Accurately predicting intracerebral hemorrhage (ICH) prognosis is a critical and indispensable step in the clinical management of patients post-ICH. Recently, integrating artificial intelligence, particularly deep learning, has significantly enhanced...