AIMC Topic: Cerebral Hemorrhage
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Machine Learning Models Prognosticate Functional Outcomes Better than Clinical Scores in Spontaneous Intracerebral Haemorrhage.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Dec 10, 2021
OBJECTIVE: This study aims to develop and compare the use of deep neural networks (DNN) and support vector machines (SVM) to clinical prognostic scores for prognosticating 30-day mortality and 90-day poor functional outcome (PFO) in spontaneous intra...
Weakly supervised multitask learning models to identify symptom onset time of unclear-onset intracerebral hemorrhage.
International journal of stroke : official journal of the International Stroke Society
Oct 17, 2021
BACKGROUND: Approximately one-third of spontaneous intracerebral hemorrhage patients did not know the onset time and were excluded from studies about time-dependent treatments for hyperacute spontaneous intracerebral hemorrhage.
A Robust Deep Learning Segmentation Method for Hematoma Volumetric Detection in Intracerebral Hemorrhage.
Stroke
Oct 4, 2021
BACKGROUND AND PURPOSE: Hematoma volume (HV) is a significant diagnosis for determining the clinical stage and therapeutic approach for intracerebral hemorrhage (ICH). The aim of this study is to develop a robust deep learning segmentation method for...
Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement.
BMC medical imaging
Aug 13, 2021
BACKGROUND: Accurate measurement of hemorrhage volume is critical for both the prediction of prognosis and the selection of appropriate clinical treatment after spontaneous intracerebral hemorrhage (ICH). This study aimed to evaluate the performance ...
Novel Approaches to Detection of Cerebral Microbleeds: Single Deep Learning Model to Achieve a Balanced Performance.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Jun 25, 2021
PURPOSE: Cerebral microbleeds (CMBs) are considered essential indicators for the diagnosis of cerebrovascular disease and cognitive disorders. Traditionally, CMBs are manually interpreted based on criteria including the shape, diameter, and signal ch...
Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion.
Clinical neuroradiology
Jun 22, 2021
PURPOSE: The objective of this study was to predict hematoma expansion (HE) by radiomic models based on different machine learning methods and determine the best radiomic model through the comparison.
Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages.
Scientific reports
May 21, 2021
Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5-40 μm) stained with Prussian blue. Currently, users manually and sub...
Can perihaematomal radiomics features predict haematoma expansion?
Clinical radiology
Apr 13, 2021
AIM: To evaluate the association between perihaematomal radiomics features and haematoma expansion (HE).
Detecting cerebral microbleeds via deep learning with features enhancement by reusing ground truth.
Computer methods and programs in biomedicine
Mar 12, 2021
BACKGROUND AND OBJECTIVES: Cerebral microbleeds (CMBs) are cerebral small vascular diseases and are often used to diagnose symptoms such as stroke and dementia. Manual detection of cerebral microbleeds is a time-consuming and error-prone task, so the...
Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage.
Translational stroke research
Feb 6, 2021
We hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical d...
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