AIMC Topic: Angiography, Digital Subtraction

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Development and Validation of an Interpretable Hemodynamics-Based Machine Learning Model for Predicting Cerebral Arteriovenous Malformation Rupture.

Translational stroke research
Cerebral arteriovenous malformation (AVM) is a cerebrovascular disease associated with a risk of intracranial hemorrhage. Currently, most risk prediction models for AVM rupture are based on demographic characteristics and lesion morphology, while qua...

Vessel diameters of 14 basal cerebral arteries assessed in 1000 digital subtraction angiographies.

Scientific data
Angiographic normative values for the size of intracranial vessels are difficult to obtain, since they vary with gender, height and weight. Cerebral angiography only is indicated in severe cerebrovascular diseases, which also can affect cerebral vess...

Gastrointestinal bleeding detection on digital subtraction angiography using convolutional neural networks with and without temporal information.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: Digital subtraction angiography (DSA) offers a real-time approach to locating lower gastrointestinal (GI) bleeding. However, many sources of bleeding are not easily visible on angiograms. This investigation aims to develop a machine learning...

Design and optimization of an automatic deep learning-based cerebral reperfusion scoring (TICI) using thrombus localization.

Journal of neuroradiology = Journal de neuroradiologie
BACKGROUND: The Thrombolysis in Cerebral Infarction (TICI) scale is widely used to assess angiographic outcomes of mechanical thrombectomy despite significant variability. Our objective was to create and optimize an artificial intelligence (AI)-based...

Generative deep-learning-model based contrast enhancement for digital subtraction angiography using a text-conditioned image-to-image model.

Computers in biology and medicine
BACKGROUND: Digital subtraction angiography (DSA) is an essential imaging technique in interventional radiology, enabling detailed visualization of blood vessels by subtracting pre- and post-contrast images. However, reduced contrast, either accident...

MRI-based radiomics model for the preoperative prediction of classification in children with venous malformations.

Clinical radiology
AIM: This study aimed to explore the efficacy of MRI-based radiomics models, employing various machine learning techniques, in the preoperative prediction of the digital subtraction angiography (DSA) classification of venous malformations (VMs).

perfDSA: Automatic Perfusion Imaging in Cerebral Digital Subtraction Angiography.

International journal of computer assisted radiology and surgery
PURPOSE: Cerebral digital subtraction angiography (DSA) is a standard imaging technique in image-guided interventions for visualizing cerebral blood flow and therapeutic guidance thanks to its high spatio-temporal resolution. To date, cerebral perfus...

Coronary p-Graph: Automatic classification and localization of coronary artery stenosis from Cardiac CTA using DSA-based annotations.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Coronary artery disease (CAD) is a prevalent cardiovascular condition with profound health implications. Digital subtraction angiography (DSA) remains the gold standard for diagnosing vascular disease, but its invasiveness and procedural demands unde...

Diagnostic Accuracy of a Deep Learning Algorithm for Detecting Unruptured Intracranial Aneurysms in Magnetic Resonance Angiography: A Multicenter Pivotal Trial.

World neurosurgery
BACKGROUND: Intracranial aneurysm rupture is associated with high mortality and disability rates. Early detection is crucial, but increasing diagnostic workloads place significant strain on radiologists. We evaluated the efficacy of a deep learning a...