François-Xavier Van Vyve¹, Karlien Carbonez², Jelena Hubrechts², Geoffroy de Beco¹, Jean E. Rubay¹, Mona Momeni³, Thierry Detaille⁴, Alain J. Poncelet^1,*

Congenital Heart Disease, Vol.18, No.5, pp. 539-550, 2023, DOI:10.32604/chd.2023.042344

Abstract Objectives: Over the last decade, neonatal repair has been advocated for many congenital heart diseases. However, specific subgroups of complex congenital heart disease still require temporary palliation for which both surgical and endovascular techniques are currently available. We reviewed our institutional experience with shunt palliation with an emphasis on risk factors for early mortality. Methods: This is a single-center retrospective study on 175 patients undergoing surgery for central shunt or modified Blalock-Taussig shunt. All data were extracted from a prospectively collected computerized database. We identified risk factors for early mortality by uni- and multi-variable analysis. All data were censored at… More >

François-Xavier Van Vyve^1,#, Karlien Carbonez^2,#, Geoffroy de Beco¹, Stéphane Moniotte², Jean E. Rubay¹, Mona Momeni³, Laurent Houtekie⁴, Alain J. Poncelet^1,*

Congenital Heart Disease, Vol.18, No.5, pp. 525-537, 2023, DOI:10.32604/chd.2023.042341

Abstract Objective: Despite increasing enthusiasm for neonatal repair, patients with ductal-dependent circulation (pulmonary/systemic) or restrictive pulmonary blood flow still require initial palliation. Ductal stenting has emerged as an endovascular approach whereas modified-Blalock-Taussig and central shunt remain surgical references. In this study, we analyzed the relationship between pulmonary artery growth, sites of shunt connection, or antegrade pulmonary blood flow in surgically placed shunts. The need for secondary catheter-based interventions or pulmonary arterioplasty was also investigated. Methods: A retrospective single-center study analyzing 175 patients undergoing surgery for a central or modified-Blalock-Taussig shunt. Outcome growth variables were right pulmonary artery/left pulmonary artery diameters/Z scores,… More > Graphic Abstract

Vitaliy Suvorov^1,2,*, Olga Loboda², Maria Balakina¹, Igor Kulczycki²

Congenital Heart Disease, Vol.18, No.5, pp. 491-505, 2023, DOI:10.32604/chd.2023.030583

Abstract Background: Three-dimensional printing technology may become a key factor in transforming clinical practice and in significant improvement of treatment outcomes. The introduction of this technique into pediatric cardiac surgery will allow us to study features of the anatomy and spatial relations of a defect and to simulate the optimal surgical repair on a printed model in every individual case. Methods: We performed the prospective cohort study which included 29 children with congenital heart defects. The hearts and the great vessels were modeled and printed out. Measurements of the same cardiac areas were taken in the same planes and points at… More > Graphic Abstract

Sidra Abbas¹, Gabriel Avelino Sampedro^2,3, Shtwai Alsubai⁴, Ahmad Almadhor⁵, Tai-hoon Kim^6,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 665-680, 2023, DOI:10.32604/cmc.2023.041031

Abstract Cardiac disease is a chronic condition that impairs the heart’s functionality. It includes conditions such as coronary artery disease, heart failure, arrhythmias, and valvular heart disease. These conditions can lead to serious complications and even be life-threatening if not detected and managed in time. Researchers have utilized Machine Learning (ML) and Deep Learning (DL) to identify heart abnormalities swiftly and consistently. Various approaches have been applied to predict and treat heart disease utilizing ML and DL. This paper proposes a Machine and Deep Learning-based Stacked Model (MDLSM) to predict heart disease accurately. ML approaches such as eXtreme Gradient Boosting (XGB),… More >

Maryam Bukhari¹, Sadaf Yasmin¹, Sheneela Naz², Mehr Yahya Durrani¹, Mubashir Javaid³, Jihoon Moon⁴, Seungmin Rho^5,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1251-1279, 2023, DOI:10.32604/cmc.2023.040329

Abstract Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics, which aid in the prevention of several diseases including heart-related abnormalities. In this context, regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram (ECG) signals has the potential to save many lives. In existing studies, several heart disease diagnostic systems are proposed by employing different state-of-the-art methods, however, improving such methods is always an intriguing area of research. Hence, in this research, a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals. The proposed framework extracts both linear and… More >

Batyrkhan Omarov^1,*, Meirzhan Baikuvekov¹, Zeinel Momynkulov², Aray Kassenkhan³, Saltanat Nuralykyzy³, Mereilim Iglikova³

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3745-3761, 2023, DOI:10.32604/cmc.2023.042627

Abstract Heart disease is a leading cause of mortality worldwide. Electrocardiograms (ECG) play a crucial role in diagnosing heart disease. However, interpreting ECG signals necessitates specialized knowledge and training. The development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease diagnosis. This research paper proposes a 3D Convolutional Long Short-Term Memory (Conv-LSTM) model for detecting heart disease using ECG signals. The proposed model combines the advantages of both convolutional neural networks (CNN) and long short-term memory (LSTM) networks. By considering both the spatial and temporal dependencies of ECG, the 3D Conv-LSTM model… More >

B. Ramesh, Kuruva Lakshmanna^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2513-2528, 2023, DOI:10.32604/cmes.2023.028944

Abstract Major chronic diseases such as Cardiovascular Disease (CVD), diabetes, and cancer impose a significant burden on people and healthcare systems around the globe. Recently, Deep Learning (DL) has shown great potential for the development of intelligent mobile Health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere. The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field. Type 2… More > Graphic Abstract

Saleh Ateeq Almutairi^*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2599-2624, 2023, DOI:10.32604/csse.2023.041609

Abstract Heart disease is a primary cause of death worldwide and is notoriously difficult to cure without a proper diagnosis. Hence, machine learning (ML) can reduce and better understand symptoms associated with heart disease. This study aims to develop a framework for the automatic and accurate classification of heart disease utilizing machine learning algorithms, grid search (GS), and the Aquila optimization algorithm. In the proposed approach, feature selection is used to identify characteristics of heart disease by using a method for dimensionality reduction. First, feature selection is accomplished with the help of the Aquila algorithm. Then, the optimal combination of the… More >

Ingo Paetsch^1,*, Roman Gebauer², Christian Paech², Frank-Thomas Riede², Sabrina Oebel¹, Andreas Bollmann¹, Christian Stehning³, Jouke Smink⁴, Ingo Daehnert², Cosima Jahnke¹

Congenital Heart Disease, Vol.18, No.3, pp. 279-294, 2023, DOI:10.32604/chd.2023.029634

Abstract Background: In congenital heart disease (CHD) patients, detailed three-dimensional anatomy depiction plays a pivotal role for diagnosis and therapeutical decision making. Hence, the present study investigated the applicability of an advanced cardiovascular magnetic resonance (CMR) whole heart imaging approach utilizing nonselective excitation and compressed sensing for anatomical assessment and interventional guidance of CHD patients in comparison to conventional dynamic CMR angiography. Methods: 86 consecutive pediatric patients and adults with congenital heart disease (age, 1 to 74 years; mean, 35 years) underwent CMR imaging including a free-breathing, ECG-triggered 3D nonselective SSFP whole heart acquisition using compressed SENSE (nsWHcs). Anatomical assessability and… More >

Siegfried Geyer^1,*, Claudia Dellas², Thomas Paul², Matthias Müller², Kambiz Norozi^2,3,4

Congenital Heart Disease, Vol.18, No.3, pp. 337-348, 2023, DOI:10.32604/chd.2023.028827

Abstract Objectives: To examine whether patients with congenital heart disease (CHD) are less likely to have a partner or children than individuals from the general population. Methods: Longitudinal study with two assessments of the same patients (n = 244) from a hospital population and controls (n = 238) from the German Socio-Economic Panel (GSOEP) using parental education, patients age, and sex as matching criteria. The first patient study was conducted between 5/2003 and 6/2004, the second one between 5/2017 and 4/2019. Controls were drawn from GSOEP-surveys 2004 and 2018. CHD-severity was classified according to type of surgery: curative, reparative, or palliative.… More > Graphic Abstract