Unlabelled: The chest radiography is used routinely by the clinician as a tool in the scan of patients with systemic arterial hypertension (SAH) to evaluate the dimensions of the heart. However the highest reported sensitivity for the evaluation of heart growth with this method is 77.3% in contrast to the transthoracic echocardiogram (TTE) that reaches between 90 to 100%. The aim of this study was assess in our population of patients with SAH, the correlation between chest radiography and the TTE in regard to cardiomegaly.

Patients And Methods: Seventy two patients with SAH and radiological cardiomegaly, graded by measuring the cardiothoracic ratio (CTR), were evaluated by transthoracic echocardiography. The Pearson's and Spearman's correlation coefficients between both methods were assessed. Significance level was set at < 0.05.

Results: Forty one (56.9%) patients were women and 31 (43.1%) were men. The age was 62.4 +/- 10 years (43-83 years). Left ventricular concentric hypertrophy (LVCH) was found in 56 (77.8%) patients. In 13 (18%) patients the left ventricular end diastolic diameter (LVEDD) was higher than the normal value. The correlation coefficient between the diastolic ventricular septal thickness (DST) and CTR was 0.285 (p < 0.05) and between the LVEDD and radiological cardiomegaly was 0.203 (p = NS).

Conclusions: In patients with SAH, the radiological evidence of cardiomegaly keeps a correlation with ventricular hypertrophy, but not with ventricular dilation.

Download full-text PDF

Source

Publication Analysis

Top Keywords

chest radiography
12
patients sah
12
patients
8
patients systemic
8
systemic arterial
8
sah radiological
8
radiological cardiomegaly
8
left ventricular
8
ventricular
5
[correlation chest
4

Similar Publications

Rising computed tomography (CT) workloads require more efficient image interpretation methods. Digitally reconstructed radiographs (DRRs), generated from CT data, may enhance workflow efficiency by enabling faster radiological assessments. Various techniques exist for generating DRRs.

View Article and Find Full Text PDF

Enhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis.

J Med Internet Res

January 2025

Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.

Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.

View Article and Find Full Text PDF

Objective: To evaluate the NEXUS Chest CT ALL decision instrument (DI) in reducing unnecessary chest CT imaging in minor blunt trauma patients while preserving high sensitivity for detecting clinically meaningful injuries. Additionally, we examined the impact of delayed presentation, chronic disease, and anticoagulation/anti-aggregation medications on trauma outcomes.

Methods: This retrospective study included 853 adult minor blunt trauma patients who underwent chest CT in the emergency department (ED) of Tel-Aviv Sourasky Medical Center between 2018 and 2022.

View Article and Find Full Text PDF

Opportunistic assessment of steatotic liver disease in lung cancer screening eligible individuals.

J Intern Med

January 2025

Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine (HIM), Boston, Massachusetts, USA.

Background: Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease.

Purpose: To opportunistically estimate SLD on lung screening chest computed tomography (CT) and investigate its prognostic value in heavy smokers participating in the National Lung Screening Trial (NLST).

Material And Methods: We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.

View Article and Find Full Text PDF

Background: Chest computed tomography (CT) is a valuable tool for diagnosing and predicting the severity of coronavirus disease 2019 (COVID-19) and assessing extrapulmonary organs. Reduced muscle mass and visceral fat accumulation are important features of a body composition phenotype in which obesity and muscle loss coexist, but their relationship with COVID-19 outcomes remains unclear. In this study, we aimed to investigate the association between the erector spinae muscle (ESM) to epicardial adipose tissue (EAT) ratio (ESM/EAT) on chest CT and disease severity in patients with COVID-19.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!