AIMC Topic: Plaque, Atherosclerotic
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Assessing the impact of ultrasound image standardization in deep learning-based segmentation of carotid plaque types.
Computer methods and programs in biomedicine
Oct 10, 2024
BACKGROUND AND OBJECTIVE: Carotid B-mode ultrasound (CBUS) imaging is often used to detect and assess atherosclerotic plaques. Doctors often need to segment plaques in the CBUS images to further examine them. Multiple studies have proposed two-dimens...
Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis.
Circulation. Cardiovascular imaging
Sep 30, 2024
BACKGROUND: Plaque quantification from coronary computed tomography angiography has emerged as a valuable predictor of cardiovascular risk. Deep learning can provide automated quantification of coronary plaque from computed tomography angiography. We...
Enhanced Plaque Stabilization Effects of Alirocumab - Insights From Artificial Intelligence-Aided Optical Coherence Tomography Analysis of the Alirocumab for Thin-Cap Fibroatheroma in Patients With Coronary Artery Disease Estimated by Optical Coherence Tomography (ALTAIR) Study.
Circulation journal : official journal of the Japanese Circulation Society
Sep 21, 2024
BACKGROUND: Proprotein convertase subtilisin/kexin type 9 inhibitors stabilize vulnerable plaque, reducing cardiovascular events. However, manual optical coherence tomography (OCT) analysis of drug efficacy is challenging because of signal attenuatio...
Automated diagnosis of atherosclerosis using multi-layer ensemble models and bio-inspired optimization in intravascular ultrasound imaging.
Medical & biological engineering & computing
Sep 18, 2024
Atherosclerosis causes heart disease by forming plaques in arterial walls. IVUS imaging provides a high-resolution cross-sectional view of coronary arteries and plaque morphology. Healthcare professionals diagnose and quantify atherosclerosis physica...
Long-term Major Adverse Cardiac Event Prediction by Computed Tomography-derived Plaque Measures and Clinical Parameters Using Machine Learning.
Internal medicine (Tokyo, Japan)
Sep 4, 2024
Objective The present study evaluated the usefulness of machine learning (ML) models with the coronary computed tomography imaging and clinical parameters for predicting major adverse cardiac events (MACEs). Methods The Nationwide Gender-specific Ath...
Deep learning model for intravascular ultrasound image segmentation with temporal consistency.
The international journal of cardiovascular imaging
Aug 27, 2024
This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Using a total of 1240 40-MHz IVUS pullbacks with 191,407 frames, the model for lumen and external elast...
An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images.
Sensors (Basel, Switzerland)
Aug 20, 2024
Stroke is the second leading cause of death and a major cause of disability around the world, and the development of atherosclerotic plaques in the carotid arteries is generally considered the leading cause of severe cerebrovascular events. In recent...
Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques.
Scientific reports
Aug 16, 2024
Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for deep learning (DL)-based image quality improvement. Conventional VWI (Conv-VWI, voxel size 0.51 × 0.51...
Mechanisms of QingRe HuoXue Formula in atherosclerosis Treatment: An integrated approach using Bioinformatics, Machine Learning, and experimental validation.
International immunopharmacology
Aug 12, 2024
BACKGROUND: Atherosclerosis (AS) is the main cause of coronary heart disease, cerebral infarction, and peripheral vascular disease. QingRe HuoXue Formula (QRHXF), a common prescription of traditional Chinese medicine, has a definite effect on the cli...
Derivation and external validation of mass spectrometry-based proteomic model using machine learning algorithms to predict plaque rupture in patients with acute coronary syndrome.
Clinica chimica acta; international journal of clinical chemistry
Aug 6, 2024
BACKGROUND: A poor prognosis is associated with atherosclerotic plaque rupture (PR) despite after conventional therapy for patients with acute coronary syndrome (ACS). Timely identification of PR improves the risk stratification and prognosis of ACS ...
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