AIMC Topic: Angiography

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A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography.

BMC medical imaging
BACKGROUND: Computed tomographic pulmonary angiography (CTPA) is the diagnostic standard for confirming pulmonary embolism (PE). Since PE is a life-threatening condition, early diagnosis and treatment are critical to avoid PE-associated morbidity and...

Intraoperative angiography of the neurovascular bundle using indocyanine green and near-infrared fluorescence improves anatomical dissection during robot-assisted radical prostatectomy: initial clinical experience.

Journal of robotic surgery
Landmark artery identification in the neurovascular bundle (NVB) is important for nerve-sparing in radical prostatectomy. We aimed to investigate intraoperative angiography using indocyanine green and near-infrared fluorescence (ICG-NIRF) during robo...

Deep Learning-Based Noise Reduction Improves Optical Coherence Tomography Angiography Imaging of Radial Peripapillary Capillaries in Advanced Glaucoma.

Current eye research
PURPOSE: We applied deep learning-based noise reduction (NR) to optical coherence tomography-angiography (OCTA) images of the radial peripapillary capillaries (RPCs) in eyes with glaucoma and investigated the usefulness of this method as an objective...

Leveraging Artificial Intelligence to Enhance Peer Review: Missed Liver Lesions on Computed Tomographic Pulmonary Angiography.

Journal of the American College of Radiology : JACR
PURPOSE: The aim of this study was to use artificial intelligence (AI) to facilitate peer review for detection of missed suspicious liver lesions (SLLs) on CT pulmonary angiographic (CTPA) examinations.

A multitask deep learning approach for pulmonary embolism detection and identification.

Scientific reports
Pulmonary embolism (PE) is a blood clot traveling to the lungs and is associated with substantial morbidity and mortality. Therefore, rapid diagnoses and treatments are essential. Chest computed tomographic pulmonary angiogram (CTPA) is the gold stan...

Perfusion Maps Acquired From Dynamic Angiography MRI Using Deep Learning Approaches.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: A typical stroke MRI protocol includes perfusion-weighted imaging (PWI) and MR angiography (MRA), requiring a second dose of contrast agent. A deep learning method to acquire both PWI and MRA with single dose can resolve this issue.

A lightweight deep learning model for automatic segmentation and analysis of ophthalmic images.

Scientific reports
Detection, diagnosis, and treatment of ophthalmic diseases depend on extraction of information (features and/or their dimensions) from the images. Deep learning (DL) model are crucial for the automation of it. Here, we report on the development of a ...

Rethinking the neighborhood information for deep learning-based optical coherence tomography angiography.

Medical physics
PURPOSE: Optical coherence tomography angiography (OCTA) is a premium imaging modality for noninvasive microvasculature studies. Deep learning networks have achieved promising results in the OCTA reconstruction task, benefiting from their powerful mo...

Automated detection of pulmonary embolism from CT-angiograms using deep learning.

BMC medical imaging
BACKGROUND: The aim of this study was to develop and evaluate a deep neural network model in the automated detection of pulmonary embolism (PE) from computed tomography pulmonary angiograms (CTPAs) using only weakly labelled training data.