AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Brain Infarction

Showing 1 to 10 of 10 articles

Clear Filters

A Hybrid Approach for Sub-Acute Ischemic Stroke Lesion Segmentation Using Random Decision Forest and Gravitational Search Algorithm.

Current medical imaging reviews
BACKGROUND: The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. We evaluate the impact of segmentation technique during the time of breaking down the capacities of the cerebrum.

Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke.

PloS one
BACKGROUND AND PURPOSE: This project assessed performance of natural language processing (NLP) and machine learning (ML) algorithms for classification of brain MRI radiology reports into acute ischemic stroke (AIS) and non-AIS phenotypes.

Machine Learning for Detecting Early Infarction in Acute Stroke with Non-Contrast-enhanced CT.

Radiology
Background Identifying the presence and extent of infarcted brain tissue at baseline plays a crucial role in the treatment of patients with acute ischemic stroke (AIS). Patients with extensive infarction are unlikely to benefit from thrombolysis or t...

Detecting the Early Infarct Core on Non-Contrast CT Images with a Deep Learning Residual Network.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
PURPOSE: To explore a new approach mainly based on deep learning residual network (ResNet) to detect infarct cores on non-contrast CT images and improve the accuracy of acute ischemic stroke diagnosis.

Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning.

Scientific reports
Deep learning (DL) is an advanced machine learning approach used in diverse areas such as bioinformatics, image analysis, and natural language processing. Here, using brain magnetic resonance imaging (MRI) data obtained at early stages of infarcts, w...

Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease.

BMC neurology
BACKGROUND: There are numerous barriers to identifying patients with silent brain infarcts (SBIs) and white matter disease (WMD) in routine clinical care. A natural language processing (NLP) algorithm may identify patients from neuroimaging reports, ...

Deep learning reconstruction for improving the visualization of acute brain infarct on computed tomography.

Neuroradiology
PURPOSE: This study aimed to investigate the impact of deep learning reconstruction (DLR) on acute infarct depiction compared with hybrid iterative reconstruction (Hybrid IR).

A new machine learning model to predict the prognosis of cardiogenic brain infarction.

Computers in biology and medicine
Cardiogenic cerebral infarction (CCI) is a disease in which the blood supply to the blood vessels in the brain is insufficient due to atherosclerosis or stenosis of the coronary arteries in the patient's heart, which leads to neurological deficits. T...

A machine learning approach to automate microinfarct and microhemorrhage screening in hematoxylin and eosin-stained human brain tissues.

Journal of neuropathology and experimental neurology
Microinfarcts and microhemorrhages are characteristic lesions of cerebrovascular disease. Although multiple studies have been published, there is no one universal standard criteria for the neuropathological assessment of cerebrovascular disease. In t...