Assessment of Attenuation in Pericarotid Fat among Patients with Carotid Plaque and Spontaneous Carotid Dissection.

Background And Purpose: Changes in perivascular fat density (PFD) and its association with inflammation have been topics of interest in both atherosclerotic and nonatherosclerotic vasculopathies. The objective of this study was to assess the PFD in patients with spontaneous internal carotid artery dissection (SICAD) or carotid atherosclerotic plaque, with and without intraplaque hemorrhage (IPH).

Materials And Methods: A cross-sectional retrospective bicentric analysis of 130 patients (30 with SICAD and 100 with carotid atherosclerotic plaque) who underwent CT angiography was performed.

Impact of papillary muscle infarction on atrial and ventricular myocardial deformation in non-anterior STEMI patients.

The purpose of this study was to explore the impact of papillary muscle (PPM) infarction on left atrial and ventricular strain parameters in patients with non-anterior ST-segment elevation myocardial infarction (NA-STEMI) using cardiovascular magnetic resonance (CMR). This retrospective study performed CMR scans on 88 consecutive patients with NA-STEMI (68 males, 65 ± 10.05 years).

Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey.

Women are disproportionately affected by chronic autoimmune diseases (AD) like systemic lupus erythematosus (SLE), scleroderma, rheumatoid arthritis (RA), and Sjögren's syndrome. Traditional evaluations often underestimate the associated cardiovascular disease (CVD) and stroke risk in women having AD. Vitamin D deficiency increases susceptibility to these conditions.

An Artificial Intelligence-Based Non-Invasive Approach for Cardiovascular Disease Risk Stratification in Obstructive Sleep Apnea Patients: A Narrative Review.

Background: Obstructive sleep apnea (OSA) is a severe condition associated with numerous cardiovascular complications, including heart failure. The complex biological and morphological relationship between OSA and atherosclerotic cardiovascular disease (ASCVD) poses challenges in predicting adverse cardiovascular outcomes. While artificial intelligence (AI) has shown potential for predicting cardiovascular disease (CVD) and stroke risks in other conditions, there is a lack of detailed, bias-free, and compressed AI models for ASCVD and stroke risk stratification in OSA patients.

Addressing hidden risks: Systematic review of artificial intelligence biases across racial and ethnic groups in cardiovascular diseases.

Background: Artificial intelligence (AI)-based models are increasingly being integrated into cardiovascular medicine. Despite promising potential, racial and ethnic biases remain a key concern regarding the development and implementation of AI models in clinical settings.

Objective: This systematic review offers an overview of the accuracy and clinical applicability of AI models for cardiovascular diagnosis and prognosis across diverse racial and ethnic groups.