Peripheral Artery Disease

Characterizing hub biomarkers for metabolic-induced endothelial dysfunction and unveiling their regulatory roles in EndMT through RNA sequencing and machine learning approaches.

BACKGROUND: Metabolic disorder and endothelial dysfunction (ED) are key events in the development and pathophysiology of atherosclerosis and are associated with an elevated risk of Cardiovascular disease (CVD). The pathophysiology remains incompletely understood.

The multikinetic fusion feature of PPG was combined with MCNN_vision_transformer for diabetes detection.

BACKGROUND: Diabetes is a chronic condition that significantly impacts the cardiovascular system and various other organs. Photoplethysmogram (PPG) signals have been shown to correlate with variations in vascular blood flow and the presence of atherosclerosis. To effectively explore the complex nonlinear relationship between PPG signals and diabetes, we propose an automatic detection model based o...

: Scanner-based image acquisition of medically important arthropods for the development of computer vision and deep learning models.

Computer vision methods offer great potential for rapid image-based identification of medically important arthropod specimens. However, imaging large ...

ML-ROM wall shear stress prediction in patient-specific vascular pathologies under a limited clinical training data regime.

High-fidelity numerical simulations such as Computational Fluid Dynamics (CFD) have been proven effective in analysing haemodynamics, offering insight...

Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration

The traditional interpretation of Intravascular Ultrasound (IVUS) images during Percutaneous Coronary Intervention (PCI) is time-intensive and incon...

A Parametric Approach to Adversarial Augmentation for Cross-Domain Iris Presentation Attack Detection

Iris-based biometric systems are vulnerable to presentation attacks (PAs), where adversaries present physical artifacts (e.g., printed iris images, ...

Toward clearer recognition and easier usefulness: development of a cross-lingual atherosclerotic cerebrovascular disease ontology.

Atherosclerotic cerebrovascular disease could result in a great number of deaths and disabilities. However, it did not acquire enough attention. Less ...

[Investigation of the impact of the deep learning based CT fractional flow reserve on clinical decision-making and long-term prognosis in patients with obstructive coronary heart disease].

To investigate the impact of the deep-learning-based CT fractional flow reserve (CT-FFR) on clinical decision-making and long-term prognosis in patie...

Advanced Plaque Modeling for Atherosclerosis Detection Using Molecular Communication

As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts...

Deep Learning and Single-Cell Sequencing Analyses Unveiling Key Molecular Features in the Progression of Carotid Atherosclerotic Plaque.

Rupture of advanced carotid atherosclerotic plaques increases the risk of ischaemic stroke, which has significant global morbidity and mortality rates...

Shaping the future of heart health.

For World Heart Day on September 24, 2024, the World Heart Federation urges nations to endorse national strategies for enhancing cardiovascular health...

Prospective deep learning-based quantitative assessment of coronary plaque by computed tomography angiography compared with intravascular ultrasound: the REVEALPLAQUE study.

AIMS: Coronary computed tomography angiography provides non-invasive assessment of coronary stenosis severity and flow impairment. Automated artificia...

Physics-informed graph neural networks for flow field estimation in carotid arteries

Hemodynamic quantities are valuable biomedical risk factors for cardiovascular pathology such as atherosclerosis. Non-invasive, in-vivo measurement ...

Bioinformatics and machine learning approaches reveal key genes and underlying molecular mechanisms of atherosclerosis: A review.

Atherosclerosis (AS) causes thickening and hardening of the arterial wall due to accumulation of extracellular matrix, cholesterol, and cells. In this...

Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders.

BACKGROUND AND HYPOTHESIS: The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PA...

Assessing Basic Emotion via Machine Learning: Comparative Analysis of Number of Basic Emotions and Algorithms.

This paper explores the use of machine learning (ML) methods to identify "clusters" of basic emotions based on pleasure, arousal, and dominance (PAD)....

Assessing brain involvement in Fabry disease with deep learning and the brain-age paradigm.

While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement ...

[Evaluation of brain age changes in patients with liver cirrhosis and hepatic encephalopathy with deep learning models based on structural magnetic resonance imaging].

To investigate the brain aging in patients with cirrhosis and hepatic encephalopathy(HE), constructed a prediction model of brain age based on deep l...

Deep Learning Models for Coronary Atherosclerosis Detection in Coronary CT Angiography.

BACKGROUND: Patients with atherosclerosis have a rather high risk of showing complications, if not diagnosed quickly and efficiently.

AI: A New Solution for Old Issues of Carotid Atherosclerotic Plaque.