AIMC Topic: Coronary Artery Disease

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Generative adversarial network augmented data for improved heart sound abnormality detection.

Computers in biology and medicine
The PhysioNet/Computing in Cardiology (CinC) Challenge 2016 dataset has driven significant advancements in automated heart sound analysis using machine learning (ML) and deep learning (DL). However, these efforts are constrained by the dataset's limi...

Machine learning in diagnosing coronary artery disease via optical pumped magnetometer magnetocardiography: a prospective cohort study.

Physiological measurement
The potential of optical pumped magnetometer magnetocardiography (OPM-MCG) for diagnosing coronary artery disease (CAD) has been initially shown, yet lacks large-scale prospective research.Using invasive coronary angiography (ICA) as a reference, we ...

Impact of Deep Learning-Based Image Conversion on Fully Automated Coronary Artery Calcium Scoring Using Thin-Slice, Sharp-Kernel, Non-Gated, Low-Dose Chest CT Scans: A Multi-Center Study.

Korean journal of radiology
OBJECTIVE: To evaluate the impact of deep learning-based image conversion on the accuracy of automated coronary artery calcium quantification using thin-slice, sharp-kernel, non-gated, low-dose chest computed tomography (LDCT) images collected from m...

Deep learning techniques for automated coronary artery segmentation and coronary artery disease detection: A systematic review of the last decade (2013-2024).

Computer methods and programs in biomedicine
BACKGROUND: Coronary artery disease (CAD) is the most common cardiovascular disease, exacting high morbidity and mortality worldwide. CAD is detected on coronary artery imaging; coronary artery segmentation (CAS) of the images is essential for corona...

Computed Tomography Advancements in Plaque Analysis: From Histology to Comprehensive Plaque Burden Assessment.

Echocardiography (Mount Kisco, N.Y.)
Advancements in coronary computed tomography angiography (CCTA) facilitated the transition from traditional histological approaches to comprehensive plaque burden assessment. Recent updates in the European Society of Cardiology (ESC) guidelines empha...

Acquisition and Reconstruction Techniques for Coronary CT Angiography: Current Status and Trends over the Past Decade.

Radiographics : a review publication of the Radiological Society of North America, Inc
Coronary CT angiography (CCTA) has been widely used as a noninvasive modality for accurate assessment of coronary artery disease (CAD) in clinical settings. However, the following limitations of CCTA remain issues of interest: motion, stair-step, and...

A lightweight graph neural network to predict long-term mortality in coronary artery disease patients: an interpretable causality-aware approach.

Journal of biomedical informatics
BACKGROUND: Coronary artery disease (CAD) causes substantial death toll in the United States and worldwide. While traditional methods for CAD mortality prediction are based on established risk factors, they have significant limitations in accuracy, a...

Systemic coagulation-inflammation index in the prediction of ISR in patients undergoing drug-eluting stents implant: A retrospective study based on multiple machine learning methods.

International journal of cardiology
BACKGROUND: The Systemic Coagulation-Inflammation index (SCI) is an innovative hematological metric that accurately reflects both coagulopathic and inflammatory dynamics. In this paper, the objective of this paper is to explain the prognostic impact ...

Derivation and validation of an artificial intelligence-based plaque burden safety cut-off for long-term acute coronary syndrome from coronary computed tomography angiography.

European heart journal. Cardiovascular Imaging
AIMS: Artificial intelligence (AI) has enabled accurate and fast plaque quantification from coronary computed tomography angiography (CCTA). However, AI detects any coronary plaque in up to 97% of patients. To avoid overdiagnosis, a plaque burden saf...

Hybrid strategy of coronary atherosclerosis characterization with T1-weighted MRI and CT angiography to non-invasively predict periprocedural myocardial injury.

European heart journal. Cardiovascular Imaging
AIMS: Coronary computed tomography angiography (CCTA) and magnetic resonance imaging (MRI) can predict periprocedural myocardial injury (PMI) after percutaneous coronary intervention (PCI). We aimed to investigate whether integrating MRI with CCTA, u...