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  • Open Access

    ARTICLE

    Multimodality Medical Image Fusion Based on Pixel Significance with Edge-Preserving Processing for Clinical Applications

    Bhawna Goyal1, Ayush Dogra2, Dawa Chyophel Lepcha1, Rajesh Singh3, Hemant Sharma4, Ahmed Alkhayyat5, Manob Jyoti Saikia6,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4317-4342, 2024, DOI:10.32604/cmc.2024.047256

    Abstract Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis. It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases. However, recent image fusion techniques have encountered several challenges, including fusion artifacts, algorithm complexity, and high computing costs. To solve these problems, this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance. First, the method employs a cross-bilateral… More >

  • Open Access

    ARTICLE

    Impact of Atrial Septal Defect Closure on Mortality in Older Patients

    Sipawath Khamplod1,2, Yodying Kaolawanich1,2, Khemajira Karaketklang3, Nithima Ratanasit1,2,*

    Congenital Heart Disease, Vol.19, No.1, pp. 93-105, 2024, DOI:10.32604/chd.2024.048631

    Abstract Background: Atrial septal defect (ASD) is a common form of adult congenital heart disease that can lead to long-term adverse outcomes if left untreated. Early closure of ASD has been associated with excellent outcomes and lower complication rates. However, there is limited evidence regarding the prognosis of ASD closure in older adults. This study aims to evaluate the mortality rates in older ASD patients with and without closure. Methods: A retrospective cohort study was conducted on patients aged 40 years or older with ASD between 2001 and 2017. Patients were followed up to assess all-cause mortality. Univariable and multivariable analyses… More > Graphic Abstract

    Impact of Atrial Septal Defect Closure on Mortality in Older Patients

  • Open Access

    EDITORIAL

    Femoral Access with Ultrasound-Guided Puncture and Z-Stitch Hemostasis for Adults with Congenital Heart Diseases Undergoing Electrophysiological Procedures

    Fu Guan1,*, Matthias Gass2, Florian Berger2, Heiko Schneider1, Firat Duru1,3, Thomas Wolber1,3,*

    Congenital Heart Disease, Vol.19, No.1, pp. 85-92, 2024, DOI:10.32604/chd.2024.047266

    Abstract Aims: Although the application of ultrasound-guided vascular puncture and Z-stitch hemostasis to manage femoral access has been widely utilized, there is limited data on this combined application in adult congenital heart disease (ACHD) patients undergoing electrophysiological (EP) procedures. We sought to evaluate the safety and efficacy of ultrasound-guided puncture and postprocedural Z-stitch hemostasis for ACHD patients undergoing EP procedures. Methods and Results: The population of ACHD patients undergoing transfemoral EP procedures at the University of Zurich Heart Center between January 2019 and December 2022 was observed and analyzed. During the study period, femoral access (left/right, arterial/venous) was performed under real-time… More >

  • Open Access

    ARTICLE

    Impact of Social Determinants of Health on Self-Perceived Resilience: An Exploratory Study of Two Cohorts of Adults with Congenital Heart Disease

    Albert Osom1, Krysta S. Barton2, Katie Sexton3,4, Lyndia Brumback1, Joyce P. Yi-Frazier4, Abby R. Rosenberg5,6, Ruth Engelberg7, Jill M. Steiner8,*

    Congenital Heart Disease, Vol.19, No.1, pp. 33-48, 2024, DOI:10.32604/chd.2024.046656

    Abstract Social determinants of health (SDOH) affect quality of life. We investigated SDOH impacts on self-perceived resilience among people with adult congenital heart disease (ACHD). Secondary analysis of data from two complementary studies: a survey study conducted May 2021–June 2022 and a qualitative study conducted June 2020–August 2021. Resilience was assessed through CD-RISC10 score (range 0–40, higher scores reflect greater self-perceived resilience) and interview responses. Sociodemographic and SDOH (education, employment, living situation, monetary stability, financial dependency, area deprivation index) data were collected by healthcare record review and self-report. We used linear regression with robust standard errors to analyze survey data and… More > Graphic Abstract

    Impact of Social Determinants of Health on Self-Perceived Resilience: An Exploratory Study of Two Cohorts of Adults with Congenital Heart Disease

  • Open Access

    CASE REPORT

    A 63-Year-Old Male with D-Transposition of the Great Arteries Who Had an Early Form of the Arterial Switch Operation

    Michael A. Rebolledo1,*, Jane S. Yao2, Jason N. Johnson1, Umar S. Boston3, Benjamin R. Waller III1

    Congenital Heart Disease, Vol.19, No.1, pp. 65-68, 2024, DOI:10.32604/chd.2024.046638

    Abstract We describe a 63-year-old male who appears to have undergone an early form of the arterial switch operation for D-transposition of the great arteries performed in the mid-1960s. We review the clinical and imaging data that support our conclusion. He had a diagnostic cardiac catheterization which demonstrated severe pulmonary hypertension responsive to epoprostenol and oxygen. Our case may represent one example of the experimental surgical work done prior to Dr. Adibe Jatene’s description of the first successful arterial switch performed in 1975. More >

  • Open Access

    ARTICLE

    Use of Patient-Specific “4D” Tele-Education to Enhance Actual and Perceived Knowledge in Congenital Heart Disease (CHD) Patients

    Molly Clarke1,*, Karin Hamann2, Nancy Klein2, Laura Olivieri3, Yue-Hin Loke2

    Congenital Heart Disease, Vol.19, No.1, pp. 5-17, 2024, DOI:10.32604/chd.2024.046328

    Abstract Background: Patients with congenital heart disease (CHD) will transition to lifelong adult congenital cardiac care. However, their structural heart disease is challenging to convey via two-dimensional drawings. This study utilized a tele-educational environment, with personalized three-dimensional (3D) modeling and health Details (3D + Details = “4D”), to improve actual and perceived knowledge, both important components of transition readiness in CHD patients. Methods: Participants aged ≥13 years with a history of CHD and cardiac magnetic resonance imaging (MRI) studies were eligible. Cardiac MRI datasets were then used to segment and create 3D heart models (using Mimics, Materialize Inc.). Participants first completed… More >

  • Open Access

    ARTICLE

    Loss to Specialized Cardiology Follow-Up in Adults Living with Congenital Heart Disease

    Cheryl Dickson1,2,4, Danielle Osborn1, David Baker1,4, Judith Fethney3, David S. Celermajer1,4, Rachael Cordina1,4,*

    Congenital Heart Disease, Vol.19, No.1, pp. 49-63, 2024, DOI:10.32604/chd.2023.044874

    Abstract Background: Much has been written about the loss to follow-up in the transition between pediatric and adult Congenital Heart Disease (CHD) care centers. Much less is understood about the loss to follow-up (LTF) after a successful transition. This is critical too, as patients lost to specialised care are more likely to experience morbidity and premature mortality. Aims: To understand the prevalence and reasons for loss to follow-up (LTF) at a large Australian Adult Congenital Heart Disease (ACHD) centre. Methods: Patients with moderate or highly complex CHD and gaps in care of >3 years (defined as LTF) were identified from a… More >

  • Open Access

    ARTICLE

    An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning

    Irfan Haider1, Muhammad Attique Khan1,*, Muhammad Nazir1, Taerang Kim2, Jae-Hyuk Cha2

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 529-554, 2024, DOI:10.32604/csse.2023.042080

    Abstract Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for… More >

  • Open Access

    ARTICLE

    Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

    Nazik Alturki1, Abdulaziz Altamimi2, Muhammad Umer3,*, Oumaima Saidani1, Amal Alshardan1, Shtwai Alsubai4, Marwan Omar5, Imran Ashraf6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3513-3534, 2024, DOI:10.32604/cmes.2023.045868

    Abstract Chronic kidney disease (CKD) is a major health concern today, requiring early and accurate diagnosis. Machine learning has emerged as a powerful tool for disease detection, and medical professionals are increasingly using ML classifier algorithms to identify CKD early. This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California, UC Irvine Machine Learning repository. The research introduces TrioNet, an ensemble model combining extreme gradient boosting, random forest, and extra tree classifier, which excels in providing highly accurate predictions for CKD. Furthermore, K nearest neighbor (KNN) imputer is utilized to deal… More >

  • Open Access

    ARTICLE

    Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

    Jinbo Yang1, Hai Huang1, Lailai Yin2, Jiaxing Qu3, Wanjuan Xie4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3085-3099, 2024, DOI:10.32604/cmes.2023.045417

    Abstract Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients’ data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the model, the computational requirements should… More >

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