AIMC Topic: Angiography

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External validation of the RSNA 2020 pulmonary embolism detection challenge winning deep learning algorithm.

European journal of radiology
PURPOSE: To evaluate the diagnostic performance and generalizability of the winning DL algorithm of the RSNA 2020 PE detection challenge to a local population using CTPA data from two hospitals.

Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction.

Scientific reports
We investigated the effect of deep learning-based image reconstruction (DLIR) compared to iterative reconstruction on image quality in CT pulmonary angiography (CTPA) for suspected pulmonary embolism (PE). For 220 patients with suspected PE, CTPA stu...

A deep learning-based algorithm improves radiology residents' diagnoses of acute pulmonary embolism on CT pulmonary angiograms.

European journal of radiology
PURPOSE: To compare radiology residents' diagnostic performances to detect pulmonary emboli (PEs) on CT pulmonary angiographies (CTPAs) with deep-learning (DL)-based algorithm support and without.

Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography Measurements.

American journal of ophthalmology
PURPOSE: To develop deep learning (DL) models estimating the central visual field (VF) from optical coherence tomography angiography (OCTA) vessel density (VD) measurements.

Applying deep learning to recognize the properties of vitreous opacity in ophthalmic ultrasound images.

Eye (London, England)
BACKGROUND: To explore the feasibility of artificial intelligence technology based on deep learning to automatically recognize the properties of vitreous opacities in ophthalmic ultrasound images.

Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke.

Nature communications
Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial n...

FSTIF-UNet: A Deep Learning-Based Method Towards Automatic Segmentation of Intracranial Aneurysms in Un-Reconstructed 3D-RA.

IEEE journal of biomedical and health informatics
Segmentation of intracranial aneurysms (IAs) is an important step for the diagnosis and treatment of IAs. However, the process by which clinicians manually recognize and localize IAs is overly labor intensive. This study aims to develop a deep-learni...

Deep-learning visualization enhancement method for optical coherence tomography angiography in dermatology.

Journal of biophotonics
Optical coherence tomography angiography (OCTA) in dermatology usually suffers from low image quality due to the highly scattering property of the skin, the complexity of cutaneous vasculature, and limited acquisition time. Deep-learning methods have...

Deep learning for artery-vein classification in optical coherence tomography angiography.

Experimental biology and medicine (Maywood, N.J.)
Major retinopathies can differentially impact the arteries and veins. Traditional fundus photography provides limited resolution for visualizing retinal vascular details. Optical coherence tomography (OCT) can provide improved resolution for retinal ...