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  • Open Access

    ARTICLE

    Real-Time Remote-Mentored Echocardiography in Management of Newborns with Critical Congenital Heart Defects

    Håvard Bjerkeseth Solvin1,2,5,*, Simone Goa Diab1,4, Ole Jakob Elle2,3, Henrik Holmstrøm1,4, Henrik Brun2,4,*

    Congenital Heart Disease, Vol.18, No.5, pp. 551-559, 2023, DOI:10.32604/chd.2023.031537

    Abstract Background: The management of suspected critical congenital heart defects (CCHD) relies on timely echocardiographic diagnosis. The availability of experienced echocardiographers is limited or even non-existent in many hospitals with obstetric units. This study evaluates remote-mentored echocardiography performed by physicians without experience in imaging of congenital heart defects (CHD). Methods: The setup included a pediatric cardiologist in a separate room, guiding a physician without experience in echocardiographic imaging of CHD in the examination of a symptomatic newborn. This remote-mentoring pair was blinded to the diagnosis of the newborn and presented with a simplified patient history. The echocardiographic images were streamed to… More > Graphic Abstract

    Real-Time Remote-Mentored Echocardiography in Management of Newborns with Critical Congenital Heart Defects

  • Open Access

    ARTICLE

    Classifying Hematoxylin and Eosin Images Using a Super-Resolution Segmentor and a Deep Ensemble Classifier

    P. Sabitha*, G. Meeragandhi

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1983-2000, 2023, DOI:10.32604/iasc.2023.034402

    Abstract Developing an automatic and credible diagnostic system to analyze the type, stage, and level of the liver cancer from Hematoxylin and Eosin (H&E) images is a very challenging and time-consuming endeavor, even for experienced pathologists, due to the non-uniform illumination and artifacts. Albeit several Machine Learning (ML) and Deep Learning (DL) approaches are employed to increase the performance of automatic liver cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real-time requirement of the diagnostic situations. In this work, we present a new Ensemble Classifier (hereafter called ECNet) to classify the H&E stained… More >

  • Open Access

    EDITORIAL

    Reflections on mentoring

    Jane W. Newburger

    Congenital Heart Disease, Vol.14, No.2, pp. 126-127, 2019, DOI:10.1111/chd.12773

    Abstract This article has no abstract. More >

  • Open Access

    ABSTRACT

    Biomechanics and Yuan-Cheng (Y. C.) Fung: A Special Tribute on His 100th Birthday

    Savio L-Y. Woo1,*, Peter C-Y Chen2

    Molecular & Cellular Biomechanics, Vol.16, Suppl.2, pp. 1-2, 2019, DOI:10.32604/mcb.2019.07631

    Abstract This article has no abstract. More >

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