AIMC Topic: Infarction

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Localization of early infarction on non-contrast CT images in acute ischemic stroke with deep learning approach.

Scientific reports Nov 9, 2023
Localization of early infarction on first-line Non-contrast computed tomogram (NCCT) guides prompt treatment to improve stroke outcome. Our previous study has shown a good performance in the identification of ischemic injury on NCCT. In the present s...
Retrospective Studies Tomography, X-Ray Computed Humans Stroke Infarction Ischemic Stroke Deep Learning
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Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks.

AJNR. American journal of neuroradiology Jul 28, 2022
BACKGROUND AND PURPOSE: Supervised deep learning is the state-of-the-art method for stroke lesion segmentation on NCCT. Supervised methods require manual lesion annotations for model development, while unsupervised deep learning methods such as gener...
Image Processing, Computer-Assisted Follow-Up Studies Deep Learning Infarction Stroke Ischemic Stroke Tomography, X-Ray Computed Humans
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Tissue outcome prediction in hyperacute ischemic stroke: Comparison of machine learning models.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism Jun 23, 2021
Machine Learning (ML) has been proposed for tissue fate prediction after acute ischemic stroke (AIS), with the aim to help treatment decision and patient management. We compared three different ML models to the clinical method based on diffusion-perf...
Infarction Reperfusion Diffusion Magnetic Resonance Imaging Ischemic Stroke Prognosis Aged Middle Aged Clinical Decision-Making Machine Learning Male Retrospective Studies Brain Humans Aged, 80 and over Predictive Value of Tests Female Follow-Up Studies
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Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning.

Sensors (Basel, Switzerland) Mar 10, 2021
Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions f...
Humans Deep Learning Magnetic Resonance Imaging Image Processing, Computer-Assisted Infarction
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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...
Brain Ischemia Infarction Diffusion Magnetic Resonance Imaging Reperfusion Humans Stroke Deep Learning
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Non-invasive localization of post-infarct ventricular tachycardia exit sites to guide ablation planning: a computational deep learning platform utilizing the 12-lead electrocardiogram and intracardiac electrograms from implanted devices.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology Feb 16, 2023
AIMS: Existing strategies that identify post-infarct ventricular tachycardia (VT) ablation target either employ invasive electrophysiological (EP) mapping or non-invasive modalities utilizing the electrocardiogram (ECG). Their success relies on local...
Humans Tachycardia, Ventricular Infarction Electrophysiologic Techniques, Cardiac Electrocardiography Catheter Ablation Deep Learning
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