Atherosclerosis

Deep learning for automated dental plaque index assessment: validation against expert evaluations.

BACKGROUND: The integration of artificial intelligence (AI) into healthcare has led to promising advancements in clinical decision-making and diagnostic accuracy. In dentistry, automated methods to evaluate oral hygiene measures, such as dental plaque detection, could improve patient care and streamline remote assessments.

Evaluating artificial intelligence models for rupture risk prediction in unruptured intracranial aneurysms: a focus on vessel geometry and hemodynamic insights.

The estimation of rupture risk in Unruptured Intracranial Aneurysm (UIA) constitutes a major area of clinical interest due to the significant morbidity and mortality rates associated with aneurysm rupture. Classic clinical models based on factors such as size and location have demonstrated limited predictive accuracy, with small aneurysms being capable of rupture and larger ones remaining stable. ...

Gene therapy and genome editing for lipoprotein disorders.

Genetic factors play a critical role in the development of lipoprotein disorders, which significantly contribute to atherosclerotic cardiovascular dis...

Predicting long-term kidney graft failure using novel multi-omic blood-based biomarkers and artificial intelligence tools.

Kidney transplantation (KT) remains the preferred treatment for end-stage renal disease. With advancements in immunosuppressive regimens and KT survei...

Identification of pyroptosis related genes and subtypes in atherosclerosis using multiomic and single cell analysis.

Atherosclerosis (AS), the leading cause of cardiovascular diseases, is a chronic inflammatory disorder involving lipid metabolism, immune dysregulatio...

Screening biomarkers related to cholesterol metabolism in osteoarthritis based on transcriptomics.

Cholesterol metabolism-related genes (CMRGs) have been associated with osteoarthritis (OA), but their specific regulatory mechanisms remain unclear. T...

A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein.

Metabolic Syndrome (MetS) comprises a clustering of conditions that significantly increase the risk of heart disease, stroke, and diabetes. Timely det...

Ensemble machine learning algorithm for anti-VEGF treatment efficacy prediction in diabetic macular edema.

BACKGROUND: Diabetic macular edema (DME) is a leading cause of vision loss in diabetes, with variable responses to anti-vascular endothelial growth fa...

Plasma proteomics profiles predict the risk of future aortic aneurysm and aortic dissection.

BACKGROUND: Aortic aneurysms and aortic dissections (AA/AD) are serious vascular conditions that often progress without symptoms and are associated wi...

Artificial intelligence in coronary CT angiography: transforming the diagnosis and risk stratification of atherosclerosis.

Coronary CT Angiography (CCTA) is essential for assessing atherosclerosis and coronary artery disease, aiding in early detection, risk prediction, and...

Endothelial Metabolic Zonation in the Vascular Network: A Spatiotemporal Blueprint for Angiogenesis.

Angiogenesis, a cornerstone of vascular development, tissue regeneration, and tumor progression, is critically orchestrated by the metabolic behavior ...

A hybrid approach to heart disease prediction using a fractional-order mathematical model and machine learning algorithm.

Heart disease remains one of the leading causes of morbidity and mortality worldwide, necessitating the development of more accurate and efficient dia...

Deep Learning for Detecting Dental Plaque and Gingivitis From Oral Photographs: A Systematic Review.

OBJECTIVES: This systematic review aimed to evaluate the performance of deep learning (DL) models in detecting dental plaque and gingivitis from red, ...

Real-world faricimab switch in France: artificial intelligence-based detection of changes in exudative signs in difficult-to-treat neovascular age-related macular degeneration.

PURPOSE: Some patients with neovascular age-related macular degeneration (nAMD) have persistent signs of exudation under treatment with intravitreal i...

Identification of potent inhibitors of potential VEGFR2: a graph neural network-based virtual screening and study.

VEGFR2 is a transmembrane tyrosine kinase receptor expressed on vascular endothelial cells and is closely associated with tumour cell growth. A compar...

Automatic Multi-Task Segmentation and Vulnerability Assessment of Carotid Plaque on Contrast-Enhanced Ultrasound Images and Videos via Deep Learning.

Intraplaque neovascularization (IPN) within carotid plaque is a crucial indicator of plaque vulnerability. Contrast-enhanced ultrasound (CEUS) is a va...

Delving into Generalizable Label Distribution Learning.

Owing to the excellent capability in dealing with label ambiguity, Label Distribution Learning (LDL), as an emerging machine learning paradigm, has re...

Association Between Automated Coronary Artery Calcium From Routine Chest Computed Tomography Scans and Cardiovascular Risk in Patients With Colorectal or Gastric Cancer.

BACKGROUND: As cardiovascular disease (CVD) is the leading cause of noncancer mortality in colorectal or gastric cancer patients, it is essential to i...

Feasibility of Ultralow-Dose CT With Deep-Learning Reconstruction for Aneurysm Diameter Measurement in Post-EVAR Follow-Up: A Prospective Comparative Study With Conventional CT.

PURPOSE: We conducted a prospective study to evaluate the usefulness of ultralow-dose computed tomography (ULD-CT) with deep-learning reconstruction (...

Can we identify individuals at risk to develop multiple myeloma? A machine learning-based predictive model.

Multiple myeloma evolves unnoticed over years, and when diagnosed, organ damage is common. Electronic health records (EHR) can help in developing pred...