Atherosclerosis

Development and validation of cardiometabolic risk predictive models based on LDL oxidation and candidate geromarkers from the MARK-AGE data.

The predictive value of the susceptibility to oxidation of LDL particles (LDLox) in cardiometabolic risk assessment is incompletely understood. The main objective of the current study was to assess its relationship with other relevant biomarkers and cardiometabolic risk factors from MARK-AGE data. A cross-sectional observational study was carried out on 1089 subjects (528 men and 561 women), aged ...

Clinical impact of deep learning-derived intravascular ultrasound characteristics in patients with deferred coronary artery.

Prognostic markers for long-term outcomes are lacking in patients with deferred (nonculprit) coronary artery lesions. This study aimed to identify the morphological criteria for predicting adverse outcomes and validate their clinical impact. Using deep learning models, we extracted geometrical parameters and maximal attenuation (or calcium) burden index (ABI-max or CBI-max) from the intravascular ...

Plasma cell-free RNA signatures of inflammatory syndromes in children.

Inflammatory syndromes, including those caused by infection, are a major cause of hospital admissions among children and are often misdiagnosed becaus...

Long-term Major Adverse Cardiac Event Prediction by Computed Tomography-derived Plaque Measures and Clinical Parameters Using Machine Learning.

Objective The present study evaluated the usefulness of machine learning (ML) models with the coronary computed tomography imaging and clinical parame...

Artificial intelligence can help detecting incidental intracranial aneurysm on routine brain MRI using TOF MRA data sets and improve the time required for analysis of these images.

PURPOSE: The aim of our study was to assess the diagnostic performance of commercially available AI software for intracranial aneurysm detection and t...

Volume Measurements for Surveillance after Endovascular Aneurysm Repair using Artificial Intelligence.

OBJECTIVE: Surveillance after endovascular aneurysm repair (EVAR) is suboptimal due to limited compliance and relatively large variability in measurem...

A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease.

BACKGROUND: Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extracti...

Artificial intelligence in corneal diseases: A narrative review.

Corneal diseases represent a growing public health burden, especially in resource-limited settings lacking access to specialized eye care. Artificial ...

Deep learning model for intravascular ultrasound image segmentation with temporal consistency.

This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Usi...

Deep Learning-Based Kinetic Analysis in Paper-Based Analytical Cartridges Integrated with Field-Effect Transistors.

This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (D...

Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.

BACKGROUND: Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decis...

Artificial Intelligence Prediction Model of Occurrence of Cerebral Vasospasms Based on Machine Learning.

BACKGROUND:  Symptomatic cerebral vasospasms are deleterious complication of the rupture of a cerebral aneurysm and potentially lethal. The existing s...

Deep reinforcement learning control of combined chemotherapy and anti-angiogenic drug delivery for cancerous tumor treatment.

By virtue of the chronic and dangerous nature of cancer, researchers have explored various approaches to managing the abnormal cell growth associated ...

An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images.

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 ca...

An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping.

Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its ...

Reduced order modelling of intracranial aneurysm flow using proper orthogonal decomposition and neural networks.

Reduced order modelling (ROMs) methods, such as proper orthogonal decomposition (POD), systematically reduce the dimensionality of high-fidelity compu...

Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques.

Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for de...

Advancing aneurysm management: the potential of AI and machine learning in enhancing safety and predictive accuracy.

Cerebral aneurysm rupture, the predominant cause of non-traumatic subarachnoid hemorrhage, underscores the need for effective treatment and early dete...

Machine learning analysis of serum cholesterol's impact on knee osteoarthritis progression.

The controversy surrounding whether serum total cholesterol is a risk factor for the graded progression of knee osteoarthritis (KOA) has prompted this...

Reproducibility and across-site transferability of an improved deep learning approach for aneurysm detection and segmentation in time-of-flight MR-angiograms.

This study aimed to (1) replicate a deep-learning-based model for cerebral aneurysm segmentation in TOF-MRAs, (2) improve the approach by testing vari...