AIMC Topic: Brain Ischemia
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Dynamic Detection of Delayed Cerebral Ischemia: A Study in 3 Centers.
Stroke
Feb 18, 2021
BACKGROUND AND PURPOSE: Delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage negatively impacts long-term recovery but is often detected too late to prevent damage. We aim to develop hourly risk scores using routinely collected cl...
Impact of the reperfusion status for predicting the final stroke infarct using deep learning.
NeuroImage. Clinical
Dec 25, 2020
BACKGROUND: Predictive maps of the final infarct may help therapeutic decisions in acute ischemic stroke patients. Our objectives were to assess whether integrating the reperfusion status into deep learning models would improve their performance, and...
A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.
Nature communications
Nov 30, 2020
Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trai...
Machine Learning to Predict Delayed Cerebral Ischemia and Outcomes in Subarachnoid Hemorrhage.
Neurology
Nov 12, 2020
OBJECTIVE: To determine whether machine learning (ML) algorithms can improve the prediction of delayed cerebral ischemia (DCI) and functional outcomes after subarachnoid hemorrhage (SAH).
Detecting Large Vessel Occlusion at Multiphase CT Angiography by Using a Deep Convolutional Neural Network.
Radiology
Sep 29, 2020
Background Large vessel occlusion (LVO) stroke is one of the most time-sensitive diagnoses in medicine and requires emergent endovascular therapy to reduce morbidity and mortality. Leveraging recent advances in deep learning may facilitate rapid dete...
Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients.
Annals of clinical and translational neurology
Sep 29, 2020
OBJECTIVE: Subarachnoid hemorrhage (SAH) is often devastating with increased early mortality, particularly in those with presumed delayed cerebral ischemia (DCI). The ability to accurately predict survival for SAH patients during the hospital course ...
The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model.
Sensors (Basel, Switzerland)
Sep 3, 2020
As is known, cerebral stroke has become one of the main diseases endangering people's health; ischaemic strokes accounts for approximately 85% of cerebral strokes. According to research, early prediction and prevention can effectively reduce the inci...
Deep Learning Based Software to Identify Large Vessel Occlusion on Noncontrast Computed Tomography.
Stroke
Aug 26, 2020
BACKGROUND AND PURPOSE: Reliable recognition of large vessel occlusion (LVO) on noncontrast computed tomography (NCCT) may accelerate identification of endovascular treatment candidates. We aim to validate a machine learning algorithm (MethinksLVO) t...
Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model.
Computer methods and programs in biomedicine
Aug 15, 2020
BACKGROUND AND OBJECTIVE: Currently, it is challenging to detect acute ischemic stroke (AIS)-related changes on computed tomography (CT) images. Therefore, we aimed to develop and evaluate an automatic AIS detection system involving a two-stage deep ...
Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports.
PloS one
Jun 19, 2020
Accurate, automated extraction of clinical stroke information from unstructured text has several important applications. ICD-9/10 codes can misclassify ischemic stroke events and do not distinguish acuity or location. Expeditious, accurate data extra...
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