AIMC Topic: Coronary Artery Disease

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Systematic screening by a heart team and a machine learning approach contribute to unraveling novel risk factors in revascularization candidates with complex coronary artery disease.

Polish archives of internal medicine
INTRODUCTION: The baseline characteristics affecting mortality following percutaneous or surgical revascularization in patients with left main and / or 3‑vessel coronary artery disease (CAD) observed in real‑world practice differ from those establish...

Development, evaluation and validation of machine learning models to predict hospitalizations of patients with coronary artery disease within the next 12 months.

International journal of medical informatics
BACKGROUND: Improved survival of patients after acute coronary syndromes, population growth, and overall life expectancy rise have led to a significant increase in the proportion of patients with stable coronary artery disease (CAD), creating a signi...

Real-time coronary artery segmentation in CAG images: A semi-supervised deep learning strategy.

Artificial intelligence in medicine
BACKGROUND: When treating patients with coronary artery disease and concurrent renal concerns, we often encounter a conundrum: how to achieve a clearer view of vascular details while minimizing the contrast and radiation doses during percutaneous cor...

Interpretation of SPECT wall motion with deep learning.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
OBJECTIVES: We sought to develop a novel deep learning (DL) workflow to interpret single-photon emission computed tomography (SPECT) wall motion.

G2ViT: Graph Neural Network-Guided Vision Transformer Enhanced Network for retinal vessel and coronary angiograph segmentation.

Neural networks : the official journal of the International Neural Network Society
Blood vessel segmentation is a crucial stage in extracting morphological characteristics of vessels for the clinical diagnosis of fundus and coronary artery disease. However, traditional convolutional neural networks (CNNs) are confined to learning l...

Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes.

The international journal of cardiovascular imaging
Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived technological advances but became one of the cornerstones of contemporary cardiovascular imaging. Its simplicity and quantitative nature established it as ...

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.

The international journal of cardiovascular imaging
The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascu...

Temporal Relationship-Aware Treadmill Exercise Test Analysis Network for Coronary Artery Disease Diagnosis.

Sensors (Basel, Switzerland)
The treadmill exercise test (TET) serves as a non-invasive method for the diagnosis of coronary artery disease (CAD). Despite its widespread use, TET reports are susceptible to external influences, heightening the risk of misdiagnosis and underdiagno...