Atherosclerosis

Advancing Coronary Risk Assessment Through Combined Radiomic, Mechanical, and Hemodynamic Analysis.

PURPOSE: Detecting vulnerable coronary plaques through coronary computed tomography angiography (CCTA) is a crucial, yet challenging task. To date, most of the proposed vulnerability markers have been studied in isolation. This study introduces the first integrated analysis combining radiomic, mechanical, and hemodynamic factors to explore their synergistic contribution to plaque vulnerability. ME...

Prolonging Sample Stability with Machine Learning Expands the Catalog of Tests Available for Mail-In Capillary Blood Collected At-Home.

Self-collection of biospecimens at-home, without specialized equipment or training, are increasingly being adopted in clinical practice due to convenience and patient preferences. However, sample instability during shipment means that remote access to common blood tests remains challenging. We hypothesized that the inaccuracy and imprecision in test results that develop because of sample instabili...

Comment on: "Assessing AI-generated patient leaflets on descemet membrane endothelial keratoplasty".

Association of allostatic load index with cognitive impairment in high-risk stroke populations.

BACKGROUND: Chronic psychosocial stress induces cumulative physiological dysregulation that accelerates biological aging and contributes to the develo...

Comparative machine learning models for early prediction of preterm birth from maternal serum biomarkers.

BACKGROUND: Preterm birth (PTB) is a major cause of neonatal morbidity and mortality. Inflammation and metabolic disruption are involved in its pathol...

Cerebrovascular 5D flow MRI.

BACKGROUND: 4D flow MRI facilitates quantification of cardiac phase-resolved blood velocity vector fields and has successfully been deployed to study ...

Features of B-mode ultrasound and contrast-enhanced ultrasound of carotid plaque based on deep learning enhance the prediction of vulnerable plaques associated with acute ischemic stroke.

OBJECTIVES: To develop an AI model using ultrasound features of carotid plaque for predicting the risk of acute ischemic stroke (AIS) and assess its e...

Subtypes of newly diagnosed type 2 diabetes and risk of complications: analysis of electronic health records in the USA.

AIMS/HYPOTHESIS: Data-driven subtyping of type 2 diabetes has not been translated into clinical practice due to the lack of routine fasting glucose an...

3D Cardiac Magnetic Resonance Substrate Features-Based Machine Learning Model for Post-myocardial Infarction Risk Stratification: a Multicenter Study.

BACKGROUND: Accurate risk stratification post-myocardial infarction (MI) remains challenging. This study aimed to develop interpretable machine learni...

Metabolic factor-based machine learning model for mortality prediction in acute hepatitis E: Development and validation from a dual-center cohort.

BACKGROUND: Hepatitis E virus (HEV) infection remains a major cause of liver failure with high short-term mortality, yet predictive models incorporati...

Integrated Human Organic Pollutant Exposome and Metabolome Analysis Reveals Biomarkers and Health Risks from Electronic Waste Exposure.

Escalating global electronic waste (e-waste) generation contrasts with <20% formal recycling rates. Policy gaps and inadequate enforcement exacerbate ...

Pericoronary Adipose Tissue Imaging: Quantitative Assessment, Artificial Intelligence Integration, and Therapeutic Modulation.

Pericoronary adipose tissue (PCAT) is increasingly recognised as a biosensor of vascular inflammation. The guideline-driven widespread adoption of cor...

Performance Analysis of Deep Learning Models for Segmentation of Carotid Artery Vessel Wall in 3D-MERGE Images.

BACKGROUND: Carotid vessel wall segmentation and determination of the lumen area are crucial for the diagnosis of atherosclerosis. U-Net-based deep le...

Development of an artificial intelligence platform to improve compliance in periodontal maintenance patients: A proof-of-concept study.

BACKGROUND: The purpose of this study is to report on the development of an artificial intelligence (AI) model designed to improve compliance in perio...

Machine Learning for Predicting Stroke Risk Stratification Using Multiomics Data: Systematic Review.

BACKGROUND: Stroke is a complex, multidimensional disorder influenced by interacting inflammatory, immune, coagulation, endothelial, and metabolic pat...

CSF proteomics identifies ADGRG1 as a predictive biomarker of intrathecal immune checkpoint inhibitor response in leptomeningeal metastasis.

BACKGROUND: Leptomeningeal metastasis (LM) is a fatal complication of advanced cancer with limited therapeutic options and poor prognosis. Immune chec...

Open-source framework for detecting bias and overfitting for large pathology images.

Even foundational models trained on large-scale datasets may learn to rely on non-relevant artifacts such as background color or color intensity, lead...

Self-supervised ultrasound B-mode strain elastography using SMURF.

Displacement tracking is an integral part of ultrasound strain elastography (USE) and various approaches have been proposed over the last three decade...

Development of a chemometric-assisted SERS method for simultaneous analysis of HDL and LDL cholesterol in blood serum with silver nanoparticles as substrate.

Cardiovascular diseases (CVD) are becoming a serious threat to human health. These are considered the leading causes of mortality. Abnormal lipid conc...

An Artificial Intelligence-Selected Multifunctional Oligopeptide Coated Biphasic Calcium Phosphate (BCP) Scaffold Promotes Bone Defect Repair.

Bone defects caused by inflammation, trauma, tumor resection, or tooth extraction present persistent clinical challenges, primarily because of the lim...