Atherosclerosis

Development and multinational validation of an algorithmic strategy for high Lp(a) screening.

Elevated lipoprotein (a) (Lp(a)) is associated with premature atherosclerotic cardiovascular disease. However, fewer than 0.5% of individuals undergo Lp(a) testing, limiting the evaluation and use of novel targeted therapeutics currently under development. Here we describe the development of a machine learning model for targeted screening for elevated Lp(a) (≥150 nmol l) in the UK Biobank (N = 456...

Deep learning-based fully automatic screening of carotid artery plaques in computed tomography angiography: a multicenter study.

AIM: To develop and validate a deep learning (DL) algorithm for the automated detection and classification of carotid artery plaques (CAPs) on computed tomography angiography (CTA) images.

Radiomics and artificial intelligence: General notions and applications in the carotid vulnerable plaque.

Carotid atherosclerosis plays a substantial role in cardiovascular morbidity and mortality. Given the multifaceted impact of this disease, there has b...

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

Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived technological advances but became one of the cornerston...

Prediction of endovascular leaks after thoracic endovascular aneurysm repair though machine learning applied to pre-procedural computed tomography angiographs.

To predict endoleaks after thoracic endovascular aneurysm repair (TEVAR) we submitted patient characteristics and vessel features observed on pre- ope...

Cryo-EM images of phase-separated lipid bilayer vesicles analyzed with a machine-learning approach.

Lateral lipid heterogeneity (i.e., raft formation) in biomembranes plays a functional role in living cells. Three-component mixtures of low- and high-...

A Deep Learning Framework for Analysis of the Eustachian Tube and the Internal Carotid Artery.

OBJECTIVE: Obtaining automated, objective 3-dimensional (3D) models of the Eustachian tube (ET) and the internal carotid artery (ICA) from computed to...

Prediction and causal inference of hyperuricemia using gut microbiota.

Hyperuricemia (HUA) is a symptom of high blood uric acid (UA) levels, which causes disorders such as gout and renal urinary calculus. Prolonged HUA is...

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

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery diseas...

A deep learning-based calculation system for plaque stenosis severity on common carotid artery of ultrasound images.

ObjectivesAssessment of plaque stenosis severity allows better management of carotid source of stroke. Our objective is to create a deep learning (DL)...

Progression from Prediabetes to Diabetes in a Diverse U.S. Population: A Machine Learning Model.

To date, there are no widely implemented machine learning (ML) models that predict progression from prediabetes to diabetes. Addressing this knowledg...

AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CAC) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis.

INTRODUCTION: Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently...

Integrating machine learning algorithms and single-cell analysis to identify gut microbiota-related macrophage biomarkers in atherosclerotic plaques.

OBJECTIVE: The relationship between macrophages and the gut microbiota in patients with atherosclerosis remains poorly defined, and effective biologic...

A comparative analysis of deep learning-based location-adaptive threshold method software against other commercially available software.

Automatic segmentation of the coronary artery using coronary computed tomography angiography (CCTA) images can facilitate several analyses related to ...

Assessment of atherosclerotic plaque burden: comparison of AI-QCT versus SIS, CAC, visual and CAD-RADS stenosis categories.

This study assesses the agreement of Artificial Intelligence-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic ...

Artificial Intelligence With Deep Learning Enables Assessment of Aortic Aneurysm Diameter and Volume Through Different Computed Tomography Phases.

The value of CT radiomics combined with deep transfer learning in predicting the nature of gallbladder polypoid lesions.

BACKGROUND: Computed tomography (CT) radiomics combined with deep transfer learning was used to identify cholesterol and adenomatous gallbladder polyp...

Deep-learning-based real-time individualization for reduce-order haemodynamic model.

The reduced-order lumped parameter model (LPM) has great computational efficiency in real-time numerical simulations of haemodynamics but is limited b...

On the relationship between various anticoagulants and robot-assisted radical prostatectomy: a single-surgeon serial analysis.

Prostate cancer patients often have other health conditions and take anticoagulants. It was believed that surgery under anticoagulants could worsen su...

Bridging the gap: robotic applications in cerebral aneurysms neurointerventions - a systematic review.

Cerebral aneurysm is a life-threatening condition, which requires high precision during the neurosurgical procedures. Increasing progress of evaluatin...