AIMC Topic: Plaque, Atherosclerotic

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A comparative analysis of deep learning-based location-adaptive threshold method software against other commercially available software.

The international journal of cardiovascular imaging
Automatic segmentation of the coronary artery using coronary computed tomography angiography (CCTA) images can facilitate several analyses related to coronary artery disease (CAD). Accurate segmentation of the lumen or plaque region is one of the mos...

Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images.

International journal of computer assisted radiology and surgery
PURPOSE: The assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fa...

Reproducibility of artificial intelligence-enabled plaque measurements between systolic and diastolic phases from coronary computed tomography angiography.

European radiology
OBJECTIVES: Current coronary CT angiography (CTA) guidelines suggest both end-systolic and mid-diastolic phases of the cardiac cycle can be used for CTA image acquisition. However, whether differences in the phase of the cardiac cycle influence coron...

Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images.

Scientific reports
Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We deve...

A novel deep learning model for a computed tomography diagnosis of coronary plaque erosion.

Scientific reports
Patients with acute coronary syndromes caused by plaque erosion might be managed conservatively without stenting. Currently, the diagnosis of plaque erosion requires an invasive imaging procedure. We sought to develop a deep learning (DL) model that ...

Real-time carotid plaque recognition from dynamic ultrasound videos based on artificial neural network.

Ultraschall in der Medizin (Stuttgart, Germany : 1980)
PURPOSE: Carotid ultrasound allows noninvasive assessment of vascular anatomy and function with real-time display. Based on the transfer learning method, a series of research results have been obtained on the optimal image recognition and analysis of...

Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography.

European radiology
OBJECTIVES: While the link between carotid plaque composition and cerebrovascular vascular (CVE) events is recognized, the role of calcium configuration remains unclear. This study aimed to develop and validate a CT angiography (CTA)-based machine le...