AIMC Topic: Plaque, Atherosclerotic
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Rapid lipid-laden plaque identification in intravascular optical coherence tomography imaging based on time-series deep learning.
Journal of biomedical optics
Oct 1, 2022
SIGNIFICANCE: Coronary heart disease has the highest rate of death and morbidity in the Western world. Atherosclerosis is an asymptomatic condition that is considered the primary cause of cardiovascular diseases. The accumulation of low-density lipop...
Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study.
The Lancet. Digital health
Apr 1, 2022
BACKGROUND: Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of p...
Stratification of carotid atheromatous plaque using interpretable deep learning methods on B-mode ultrasound images.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Nov 1, 2021
Carotid atherosclerosis is the major cause of ischemic stroke resulting in significant rates of mortality and disability annually. Early diagnosis of such cases is of great importance, since it enables clinicians to apply a more effective treatment s...
Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence.
Open heart
Nov 1, 2021
OBJECTIVE: The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT).
Prediction of atherosclerotic disease progression combining computational modelling with machine learning.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2020
Non-invasive serial computed tomography coronary angiography (CTCA) was acquired from 32 patients and 3D reconstruction of 58 coronary arteries was achieved. The arterial geometries were utilized for blood flow and LDL transport modelling. Navier-Sto...
Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture.
Ultrasonic imaging
Jan 1, 2020
Carotid plaque segmentation in ultrasound longitudinal B-mode images using deep learning is presented in this work. We report on 101 severely stenotic carotid plaque patients. A standard U-Net is compared with a dilated U-Net architecture in which th...
A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography.
European heart journal
Nov 14, 2019
BACKGROUND: Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). Howeve...
Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets.
Journal of biomedical optics
Oct 1, 2019
We developed machine learning methods to identify fibrolipidic and fibrocalcific A-lines in intravascular optical coherence tomography (IVOCT) images using a comprehensive set of handcrafted features. We incorporated features developed in previous st...
Optical Coherence Tomography Vulnerable Plaque Segmentation Based on Deep Residual U-Net.
Reviews in cardiovascular medicine
Sep 30, 2019
Automatic and accurate segmentation of intravascular optical coherence tomography imagery is of great importance in computer-aided diagnosis and in treatment of cardiovascular diseases. However, this task has not been well addressed for two reasons. ...
Expert curation for building network-based dynamical models: a case study on atherosclerotic plaque formation.
Database : the journal of biological databases and curation
Jan 1, 2018
http://biomodels.caltech.edu.
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