AIMC Topic: Angiography, Digital Subtraction
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Artificial intelligence assistance improves the accuracy and efficiency of intracranial aneurysm detection with CT angiography.
European journal of radiology
Jan 20, 2022
PURPOSE: The aim of this study was to evaluate whether a novel head and neck artificial intelligence (AI)-assisted diagnostic system based on a three-dimensional convolutional neural network (3D-CNN) could improve the accuracy, efficiency and working...
Digital Subtraction Angiography Image Features under the Deep Learning Algorithm in Cardiovascular Interventional Treatment and Nursing for Vascular Restenosis.
Computational and mathematical methods in medicine
Jan 17, 2022
The objective of this study was to explore the application value of digital subtraction angiography (DSA) images optimized by deep learning algorithms in vascular restenosis patients undergoing cardiovascular intervention and their nursing efficacy. ...
Deep Learning-Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study.
Stroke
Sep 9, 2021
BACKGROUND AND PURPOSE: Mechanical thrombectomy is an established procedure for treatment of acute ischemic stroke. Mechanical thrombectomy success is commonly assessed by the Thrombolysis in Cerebral Infarction (TICI) score, assigned by visual inspe...
Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage.
Neuroradiology
Apr 10, 2021
PURPOSE: To evaluate whether a deep learning model (DLM) could increase the detection sensitivity of radiologists for intracranial aneurysms on CT angiography (CTA) in aneurysmal subarachnoid hemorrhage (aSAH).
Deep Learning-based Angiogram Generation Model for Cerebral Angiography without Misregistration Artifacts.
Radiology
Mar 30, 2021
Background Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement-caused misregistration artifacts can result in unclear DSA images that interrupt procedures. Purpose T...
Deep learning-based classification of lower extremity arterial stenosis in computed tomography angiography.
European journal of radiology
Jan 8, 2021
PURPOSE: The purpose of this study is to develop and evaluate a deep learning model to assist radiologists in classifying lower extremity arteries based on the degree of arterial stenosis caused by plaque in lower extremity computed tomography angiog...
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...
Deep learning based detection of intracranial aneurysms on digital subtraction angiography: A feasibility study.
The neuroradiology journal
Jul 7, 2020
BACKGROUND: Digital subtraction angiography is the gold standard for detecting and characterising aneurysms. Here, we assess the feasibility of commercial-grade deep learning software for the detection of intracranial aneurysms on whole-brain anterop...
Fully automated intracranial aneurysm detection and segmentation from digital subtraction angiography series using an end-to-end spatiotemporal deep neural network.
Journal of neurointerventional surgery
May 29, 2020
BACKGROUND: Intracranial aneurysms (IAs) are common in the population and may cause death.
Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease.
The British journal of radiology
Mar 25, 2020
OBJECTIVE: To investigate the diagnostic performance of deep learning (DL)-based vascular extraction and stenosis detection technology in assessing coronary artery disease (CAD).
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