Radiological Society of North America (RSNA) 3D printing Special Interest Group (SIG): Guidelines for medical 3D printing and appropriateness for clinical scenarios

Este número da revista Cadernos de Estudos Sociais estava em organização quando fomos colhidos pela morte do sociólogo Ernesto Laclau. Seu falecimento em 13 de abril de 2014 surpreendeu a todos, e particularmente ao editor Joanildo Burity, que foi seu orientando de doutorado na University of Essex, Inglaterra, e que recentemente o trouxe à Fundação Joaquim Nabuco para uma palestra, permitindo que muitos pudessem dialogar com um dos grandes intelectuais latinoamericanos contemporâneos. Assim, buscamos fazer uma homenagem ao sociólogo argentino publicando uma entrevista inédita concedida durante a sua passagem pelo Recife, em 2013, encerrando essa revista com uma sessão especial sobre a sua trajetória

Atrial Septal Defect

Atrial Septal Defects (ASDs) are relatively common both in children and adults. Recent reports of increase in the prevalence of ASD may be related use of color Doppler echocardiography. The etiology of the ASD is largely unknown. While the majority of the book addresses closure of ASDs, one chapter in particular focuses on creating atrial defects in the fetus with hypoplastic left heart syndrome. This book, I hope, will give the needed knowledge to the physician caring for infants, children, adults and elderly with ASD which may help them provide best possible care for their patients

Focal Spot, Fall 1984

https://digitalcommons.wustl.edu/focal_spot_archives/1038/thumbnail.jp

Three-dimensional echocardiographic virtual endoscopy for the diagnosis of congenital heart disease in children

Virtual endoscopy (VE) is a new post-processing method that uses volumetric data sets to simulate the tracks of a “conventional” flexible endoscope. However, almost all studies of this method have involved virtual visualizations of the cardiovascular structures applied to computed tomography (CT) and magnetic resonance (MR) datasets. This paper introduces a novel visualization method called the “three-dimensional echocardiographic intracardiac endoscopic simulation system (3DE IESS)”, which uses 3D echocardiographic images in a virtual reality (VR) environment to diagnose congenital heart disease. The aim of this study was to analyze the feasibility of VE in the evaluation of congenital heart disease in children and its accuracy compared with 2DE. Three experienced pediatric cardiologists blinded to the patients’ diagnoses separately reviewed 40 two-dimensional echocardiographic (2DE) datasets and 40 corresponding VE datasets and judged whether abnormal intracardiac anatomy was present in terms of a five-point scale (1 = definitely absent; 2 = probably absent; 3 = cannot be determined; 4 = probably present; and 5 = definitely present). Compared with clinical diagnosis, the diagnostic accuracy of VE was 98.7% for ASD, 92.4% for VSD, 92.6% for TOF, and 94% for DORV, respectively. Diagnostic accuracy of VE was significantly higher than that of 2DE for TOF and DORV except for ASD and VSD. The receiver operating characteristic (ROC) curve for VE was closer to the optimal performance point than was the ROC curve for 2DE. The area under the ROC curve was 0.96 for VE and 0.93 for 2DE. Kappa values (range, 0.73–0.79) for VE and 2DE indicated substantial agreement. 3D echocardiographic VE can enhance our understanding of intracardiac structures and facilitate the evaluation of congenital heart disease

Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child's outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field

Semi-Automatic Measurement of Fetal Cardiac Axis in Fetuses with Congenital Heart Disease (CHD) with Fetal Intelligent Navigation Echocardiography (FINE)

Congenital heart disease (CHD) is one of the most common organ-specific birth defects and a major cause of infant morbidity and mortality. Despite ultrasound screening guidelines, the detection rate of CHD is limited. Fetal intelligent navigation echocardiography (FINE) has been introduced to extract reference planes and cardiac axis from cardiac spatiotemporal image correlation (STIC) volume datasets. This study analyses the cardiac axis in fetuses affected by CHD/thoracic masses (n = 545) compared to healthy fetuses (n = 1543) generated by FINE. After marking seven anatomical structures, the FINE software generated semi-automatically nine echocardiography standard planes and calculated the cardiac axis. Our study reveals that depending on the type of CHD, the cardiac axis varies. In approximately 86% (471 of 542 volumes) of our pathological cases, an abnormal cardiac axis (normal median = 40-45(degrees)) was detectable. Significant differences between the fetal axis of the normal heart versus CHD were detected in HLHS, pulmonary atresia, TOF (p-value < 0.0001), RAA, situs ambiguus (p-value = 0.0001-0.001) and absent pulmonary valve syndrome, DORV, thoracic masses (p-value = 0.001-0.01). This analysis confirms that in fetuses with CHD, the cardiac axis can significantly deviate from the normal range. FINE appears to be a valuable tool to identify cardiac defects

Fetal Intelligent Navigation Echocardiography (FINE) Detects 98% of Congenital Heart Disease

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146329/1/jum14616.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146329/2/jum14616_am.pd