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.

MultiNet 2.0: A lightweight attention-based deep learning network for stenosis measurement in carotid ultrasound scans and cardiovascular risk assessment.

Background: Cardiovascular diseases (CVD) cause 19 million fatalities each year and cost nations billions of dollars. Surrogate biomarkers are established methods for CVD risk stratification; however, manual inspection is costly, cumbersome, and error-prone. The contemporary artificial intelligence (AI) tools for segmentation and risk prediction, including older deep learning (DL) networks employ simple merge connections which may result in semantic loss of information and hence low in accuracy.

Neuroimaging evaluations of olfactory, gustatory, and neurological deficits in patients with long-term sequelae of COVID-19.

The World Health Organization indicated that around 36 million of patients in the European Region showed long COVID associated with olfactory and gustatory deficits. The precise mechanism underlying long COVID clinical manifestations is still debated. The aim of this study was to evaluate potential correlations between odor threshold, odor discrimination, odor identification, and the activation of specific brain areas in patients after COVID-19.

Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.

Background: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0 (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD.

Pericarotid Fat as a Marker of Cerebrovascular Risk.

Vascular inflammation is widely recognized as an important factor in the atherosclerotic process, particularly in terms of plaque development and progression. Conventional tests, such as measuring circulating inflammatory biomarkers, lack the precision to identify specific areas of vascular inflammation. In this context, noninvasive imaging modalities can detect perivascular fat changes, serving as a marker of vascular inflammation.

UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.

Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease's progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk.

Atrial and Ventricular Involvement in Acute Myocarditis Patients with Preserved Ejection Fraction: A Single-Center Cardiovascular Magnetic Resonance Study.

Cardiac magnetic resonance (CMR) is commonly employed to confirm the diagnosis of acute myocarditis (AM). However, the impact of atrial and ventricular function in AM patients with preserved ejection fraction (EF) deserves further investigation. Therefore, the aim of this study was to explore the incremental diagnostic value of combining atrial and strain functions using CMR in patients with AM and preserved EF.

Hippocampus and olfactory impairment in Parkinson disease: a comparative exploratory combined volumetric/functional MRI study.

Introduction: Patients with Parkinson's Disease (PD) commonly experience Olfactory Dysfunction (OD). Our exploratory study examined hippocampal volumetric and resting-state functional magnetic resonance imaging (rs-fMRI) variations in a Healthy Control (HC) group versus a cognitively normal PD group, further categorized into PD with No/Mild Hyposmia (PD-N/MH) and PD with Severe Hyposmia (PD-SH).

Methods: We calculated participants' relative Total Hippocampal Volume (rTHV) and performed Spearman's partial correlations, controlled for age and gender, to examine the correlation between rTHV and olfactory performance assessed by the Odor Stick Identification Test for the Japanese (OSIT-J) score.

COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography.

Background And Novelty: When RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance.

Impact of different hormones on the regulation of nitric oxide in diabetes.

Polymetabolic syndrome achieved pandemic proportions and dramatically influenced public health systems functioning worldwide. Chronic vascular complications are the major contributors to increased morbidity, disability, and mortality rates in diabetes patients. Nitric oxide (NO) is among the most important vascular bed function regulators.

Prognostic role of cardiovascular magnetic resonance in Takotsubo syndrome: A systematic review.

Background: Takotsubo syndrome (TS) is characterized by transient myocardial dysfunction with outcomes ranging from favorable to life-threatening. Cardiovascular magnetic resonance (CMR) has emerged as an essential tool in its diagnosis and management and is consistently recommended by current guidelines in the diagnostic work-up. However, the prognostic value of CMR in patients with TS remains undetermined.

Machine Learning Detects Symptomatic Plaques in Patients With Carotid Atherosclerosis on CT Angiography.

Background: This study aimed to develop and validate a computed tomography angiography based machine learning model that uses plaque composition data and degree of carotid stenosis to detect symptomatic carotid plaques in patients with carotid atherosclerosis.

Methods: The machine learning based model was trained using degree of stenosis and the volumes of 13 computed tomography angiography derived intracarotid plaque subcomponents (eg, lipid, intraplaque hemorrhage, calcium) to identify plaques associated with cerebrovascular events. The model was internally validated through repeated 10-fold cross-validation and tested on a dedicated testing cohort according to discrimination and calibration.

Atherosclerotic carotid artery disease Radiomics: A systematic review with meta-analysis and radiomic quality score assessment.

Background: Stroke, a leading global cause of mortality and neurological disability, is often associated with atherosclerotic carotid artery disease. Distinguishing between symptomatic and asymptomatic carotid artery disease is crucial for appropriate treatment decisions. Radiomics, a quantitative image analysis technique, and ML have emerged as promising tools in medical imaging, including neuroradiology.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).

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 been increasing interest in harnessing artificial intelligence (AI) and radiomics as complementary tools for the quantitative analysis of medical imaging data. This integrated approach holds promise not only in refining medical imaging data analysis but also in optimizing the utilization of radiologists' expertise.

Carotid artery calcium score: Definition, classification, application, and limits.

Introduction: In the current paper, the "carotid artery calcium score" method is presented with the target to offer a metric method to quantify the amount of calcification in the carotid artery.

Model And Definition: The Volume of Interest (VOI) should be extracted and those voxels, with a Hounsfield Unit (HU) value ≥130, should be considered. The total weight value is determined by calculating the sum of the HU attenuation values of all voxels with values ≥130 HU.