Peripheral Artery Disease

Artificial Intelligence-Based ABI Dynamic Fluctuation Patterns Predict Adverse Vascular Events in PAD: A Multicenter Prospective Study.

BACKGROUND: To develop an artificial intelligence-based predictive model using ankle-brachial index (ABI) dynamic fluctuation patterns and evaluate its predictive value for major adverse limb events (MALEs) in patients with peripheral arterial disease (PAD), thereby providing a novel risk stratification tool for precision medicine. METHODS: This multicenter prospective cohort study enrolled 412 co...

FairDITA: Disentangled Image-Text Alignment for Fair Skin Cancer Diagnosis.

Recent advances in deep learning have significantly improved skin cancer classification, yet concerns regarding algorithmic fairness persist because of performance disparities across skin tone groups. Existing methods often attempt to mitigate bias by suppressing sensitive attributes within images. However, they are fundamentally limited by the entanglement of lesion characteristics and skin tone ...

Development of an interpretable machine learning model to predict short-term bleeding risk in patients receiving dual antithrombotic therapy following cardiac surgery.

INTRODUCTION: Bleeding is a serious complication in cardiac surgery, especially among patients receiving combined anticoagulant and antiplatelet thera...

Coronary CT angiography evaluation with artificial intelligence for individualized medical treatment of atherosclerosis: a Consensus Statement from the QCI Study Group.

Coronary CT angiography is widely implemented, with an estimated 2.2 million procedures in patients with stable chest pain every year in Europe alone....

Latent Class Analysis Identifies Distinct Patient Phenotypes Associated With Mistaken Treatment Decisions and Adverse Outcomes in Coronary Artery Disease.

This study aimed to identify patient characteristics linked to mistaken treatments and major adverse cardiovascular events (MACE) in percutaneous coro...

Incidental Finding of Coronary and Non-Coronary Artery Calcium: What Do Clinicians Need To Know?

PURPOSE OF REVIEW: This review summarizes the role of incidentally and non-incidentally discovered coronary artery calcification (CAC) and the evolvin...

Residual capsule network with threshold convolution and attention mechanism for forest fire detection using UAV imagery.

Wildfires pose a severe threat to ecosystems, economies, and human lives, exemplified by the 2019 Australian bushfires, which devastated 46 million ac...

AI-driven point cloud framework for predicting solder joint reliability using 3D FEA data.

Crack propagation in solder joints remains a major challenge impacting the thermo-mechanical reliability of electronic devices, underscoring the impor...

Quantitative CT Imaging in Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous condition characterized by diverse pulmonary and extrapulmonary manifestations....

Divide-and-conquer strategy with engineered ossification center organoids for rapid bone healing through developmental cell recruitment.

Current approaches for bone repair predominantly target localized delivery of growth factors that are aimed at the coupling of angiogenesis and osteog...

Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study.

BACKGROUND: Latent autoimmune diabetes in adults (LADA) is a slowly progressing form of diabetes with autoimmune origins. Patients with LADA are at an...

From lipids and inflammation to artificial intelligence-driven therapeutics: the 93rd European atherosclerosis society (EAS) congress 2025 in focus.

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...

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...

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...

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...

From "time is brain" to "time is collaterals": updates on the role of cerebral collateral circulation in stroke.

BACKGROUND: Acute ischemic stroke (AIS) remains the leading cause of mortality and disability worldwide. While revascularization therapies-such as int...

Preventing pressure ulcers by increasing pressure: An unorthodox alternating-pressure mattress.

Despite the devastating effects of pressure ulcers (PUs), little is understood about how they can be prevented using alternating-pressure (AP) mattres...

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...

Does restrictive anorexia nervosa impact brain aging? A machine learning approach to estimate age based on brain structure.

Anorexia nervosa (AN), a severe eating disorder marked by extreme weight loss and malnutrition, leads to significant alterations in brain structure. T...