AIMC Topic: Angiography, Digital Subtraction
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Feasibility study for use of angiographic parametric imaging and deep neural networks for intracranial aneurysm occlusion prediction.
Journal of neurointerventional surgery
Dec 10, 2019
BACKGROUND: Angiographic parametric imaging (API), based on digital subtraction angiography (DSA), is a quantitative imaging tool that may be used to extract contrast flow parameters related to hemodynamic conditions in abnormal pathologies such as i...
Automatic detection on intracranial aneurysm from digital subtraction angiography with cascade convolutional neural networks.
Biomedical engineering online
Nov 14, 2019
BACKGROUND: An intracranial aneurysm is a cerebrovascular disorder that can result in various diseases. Clinically, diagnosis of an intracranial aneurysm utilizes digital subtraction angiography (DSA) modality as gold standard. The existing automatic...
A neural network approach to segment brain blood vessels in digital subtraction angiography.
Computer methods and programs in biomedicine
Nov 2, 2019
BACKGROUND AND OBJECTIVE: Cerebrovascular diseases (CVDs) affect a large number of patients and often have devastating outcomes. The hallmarks of CVDs are the abnormalities formed on brain blood vessels, including protrusions, narrows, widening, and ...
Evaluation of an Artificial Intelligence-Based 3D-Angiography for Visualization of Cerebral Vasculature.
Clinical neuroradiology
Oct 9, 2019
PURPOSE: The three-dimensional digital subtraction angiography (3D DSA) technique is the current standard and is based on both mask and fill runs to enable the subtraction technique. Artificial intelligence (AI)-based 3D angiography (3DA) was develop...
Automatic radiomic feature extraction using deep learning for angiographic parametric imaging of intracranial aneurysms.
Journal of neurointerventional surgery
Aug 23, 2019
BACKGROUND: Angiographic parametric imaging (API) is an imaging method that uses digital subtraction angiography (DSA) to characterize contrast media dynamics throughout the vasculature. This requires manual placement of a region of interest over a l...
Deep learning-based digital subtraction angiography image generation.
International journal of computer assisted radiology and surgery
Jul 31, 2019
PURPOSE: Digital subtraction angiography (DSA) is a powerful technique for diagnosing cardiovascular disease. In order to avoid image artifacts caused by patient movement during imaging, we take deep learning-based methods to generate DSA image from ...
Multitask Deep Learning for Automated Detection of Endoleak at Digital Subtraction Angiography during Endovascular Aneurysm Repair.
Radiology. Artificial intelligence
Jul 1, 2025
Purpose To develop and evaluate a novel multitask deep learning framework for automated detection and localization of endoleaks at aortic digital subtraction angiography (DSA) performed during real-world endovascular aneurysm repair (EVAR) procedures...
Advancing Intracranial Aneurysm Detection: A Comprehensive Systematic Review and Meta-analysis of Deep Learning Models Performance, Clinical Integration, and Future Directions.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Jun 1, 2025
BACKGROUND: Cerebral aneurysms pose a significant risk to patient safety, particularly when ruptured, emphasizing the need for early detection and accurate prediction. Traditional diagnostic methods, reliant on clinician-based evaluations, face chall...
Development and Validation of a Sham-AI Model for Intracranial Aneurysm Detection at CT Angiography.
Radiology. Artificial intelligence
May 1, 2025
Purpose To evaluate a sham-artificial intelligence (AI) model acting as a placebo control for a standard-AI model for diagnosis of intracranial aneurysm. Materials and Methods This retrospective crossover, blinded, multireader, multicase study was co...
Knowledge-Augmented Deep Learning for Segmenting and Detecting Cerebral Aneurysms With CT Angiography: A Multicenter Study.
Radiology
Aug 1, 2024
Background Deep learning (DL) could improve the labor-intensive, challenging processes of diagnosing cerebral aneurysms but requires large multicenter data sets. Purpose To construct a DL model using a multicenter data set for accurate cerebral aneur...
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