Peripheral Artery Disease

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

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 emphasize CCTA's role in managing chronic coronary syndrome by enabling detailed monitoring of atherosclerotic plaque progression. Limitations of conventio...

A comparative analysis of metamodels for 0D cardiovascular models, and pipeline for sensitivity analysis, parameter estimation, and uncertainty quantification.

Zero-dimensional (0D) cardiovascular models are reduced-order models aimed at studying the global dynamics of the whole circulation system or transport within it. They are employed to obtain estimates of important biomarkers for surgery planning and assessment applications (such as pressures, volumes, flow rates, and concentrations in the circulation) and can provide boundary conditions for high-f...

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

AIMS: Coronary computed tomography angiography (CCTA) and magnetic resonance imaging (MRI) can predict periprocedural myocardial injury (PMI) after pe...

Can Foundation Models Generalise the Presentation Attack Detection Capabilities on ID Cards?

Nowadays, one of the main challenges in presentation attack detection (PAD) on ID cards is obtaining generalisation capabilities for a diversity of ...

Assessing the Global Impact of Brain Small Vessel Disease on Cognition: The Multi-Ethnic Study of Atherosclerosis.

INTRODUCTION: We aimed to examine the global impact of brain small vessel disease (SVD) on cognitive performance.

PET imaging of atherosclerosis: artificial intelligence applications and recent advancements.

PET imaging has become a valuable tool for assessing atherosclerosis by targeting key processes such as inflammation and microcalcification. Among ava...

The role of mitochondrial dysfunction in the pathogenesis of atherosclerosis: A new exploration from bioinformatics analysis.

Atherosclerosis (AS) is a complex cardiovascular disease associated with mitochondrial dysfunction (MD), which contributes to plaque formation and ins...

Elucidating the role of KLRD1 in coronary atherosclerosis: harnessing bioinformatics and machine learning to advance understanding.

BACKGROUND: Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and serve...

Diagnostic value of small dense low-density lipoprotein and trace elements in coronary artery disease.

Coronary artery disease (CAD) is a worldwide leading cause of death. Considering that 20%-40% of patients with CAD have a long asymptomatic period of ...

Recognizing artery segments on carotid ultrasonography using embedding concatenation of deep image and vision-language models.

Evaluating large artery atherosclerosis is critical for predicting and preventing ischemic strokes. Ultrasonographic assessment of the carotid arterie...

Machine learning using scRNA-seq Combined with bulk-seq to identify lactylation-related hub genes in carotid arteriosclerosis.

Atherosclerosis is a chronic inflammatory disease, this study aims to investigate the immune landscape in carotid atherosclerotic plaque formation and...

Detection of carotid artery calcifications using artificial intelligence in dental radiographs: a systematic review and meta-analysis.

BACKGROUND: Carotid artery calcifications are important markers of cardiovascular health, often associated with atherosclerosis and a higher risk of s...

Evaluating the Robustness of Adversarial Defenses in Malware Detection Systems

Machine learning is a key tool for Android malware detection, effectively identifying malicious patterns in apps. However, ML-based detectors are vu...

Comparative Performance of Clinician and Computational Approaches in Forecasting Adverse Outcomes in Intermittent Claudication.

BACKGROUND: Recent evidence has shown that machine learning (ML) techniques can accurately forecast adverse cardiovascular and limb events in patients...

Acute coronary syndromes: mechanisms, challenges, and new opportunities.

Despite advances in research and patient management, atherosclerosis and its dreaded acute and chronic sequelae continue to account for one out of thr...

SynID: Passport Synthetic Dataset for Presentation Attack Detection

The demand for Presentation Attack Detection (PAD) to identify fraudulent ID documents in remote verification systems has significantly risen in rec...

A deep learning and molecular modeling approach to repurposing Cangrelor as a potential inhibitor of Nipah virus.

Deforestation, urbanization, and climate change have significantly increased the risk of zoonotic diseases. Nipah virus (NiV) of Paramyxoviridae famil...

Combined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases across different glucose metabolic states: an observational cohort study with machine learning.

BACKGROUND: Stress hyperglycemia ratio (SHR) and glycemic variability (GV) reflect acute glucose elevation and fluctuations, which correlate with adve...

Shortcut learning leads to sex bias in deep learning models for photoacoustic tomography.

PURPOSE: Shortcut learning has been identified as a source of algorithmic unfairness in medical imaging artificial intelligence (AI), but its impact o...

Eliciting the Impact of Metformin and Statins on Prostate Cancer Outcomes from a Real-life National Database Analysis.

Several large analyses have revealed contradictory results regarding the association between prostate cancer (PC) survival and the use of statins pres...