Nuttanicha Sriboonyawattana^*, Thitima Suklerttrakul

Congenital Heart Disease, Vol.20, No.2, pp. 231-244, 2025, DOI:10.32604/chd.2025.064662 - 30 April 2025

Abstract Objective: Many children with fully corrected congenital heart disease (CHD) avoid physical activity (PA). This descriptive study sought to determine child and parental factors that could predict PA levels in Thai children after corrective surgery. Methods: Ninety school-aged children with fully corrected CHD were recruited from a cardiology clinic at a university hospital in northern Thailand. Data collection involved five validated questionnaires: (1) the Modified Thai Adolescent’s Physical Activity Questionnaire, (2) the Child Health Status Questionnaire-Forms I and II, (3) the Parental Knowledge on School-aged Children’s Physical Activity Scale, (4) the Perceived Self-efficacy to Physical Activity… More >

Hongqun Xiang¹, Jian Zhuang^2,3, Luoning Bao^4,5, Yan Shi^2,3,*

Congenital Heart Disease, Vol.20, No.2, pp. 245-263, 2025, DOI:10.32604/chd.2025.064366 - 30 April 2025

Abstract Congenital heart disease (CHD) is the most common birth defect, with 34% of cases attributed to genetic variants. NOTCH1, a multi-domain transmembrane protein, regulates heart development by controlling the differentiation and migration of myocardial mesoderm cells, and different variants are present in different types of CHD. In this review, we aim to provide a detailed description of NOTCH1 structural domains and their functions, highlighting NOTCH1 variants in CHD and the molecular mechanisms through which they contribute to CHD occurrence. NOTCH1 has two main domains, the NOTCH extracellular domain (NECD) and the NOTCH intracellular domain… More >

Dina Anggraini¹, Kurnia Wahyudi², Melawati Hasan³, Sri Endah Rahayuningsih^4,*, Charlotte Johanna Cool³

Congenital Heart Disease, Vol.20, No.2, pp. 133-141, 2025, DOI:10.32604/chd.2025.064164 - 30 April 2025

Abstract Background: Congenital heart disease (CHD) occurs in 9 out of 100 births and is the leading cause of birth defects, with acyanotic CHD being more common. The incidence of adult CHD is rising faster than pediatric CHD. Pulmonary hypertension is the most common complication in untreated CHD patients. Methods: This study is retrospective descriptive research based on medical record data and the results of right heart catheterization examinations in adult acyanotic CHD aged ≥18 years and free from other organ disorders. Results: A total of 103 patients met the inclusion criteria, the majority were young… More >

Chunlai Du^1,#, Xin Gu^1,#, Yanhui Guo^2,*, Siqi Guo³, Ziwei Pang³, Yi Du³, Guoqing Du^3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3435-3450, 2025, DOI:10.32604/cmc.2025.060212 - 16 April 2025

Abstract Coronary artery disease is a highly lethal cardiovascular condition, making early diagnosis crucial for patients. Echocardiograph is employed to identify coronary heart disease (CHD). However, due to issues such as fuzzy object boundaries, complex tissue structures, and motion artifacts in ultrasound images, it is challenging to detect CHD accurately. This paper proposes an improved Transformer model based on the Feedback Self-Attention Mechanism (FSAM) for classification of ultrasound images. The model enhances attention weights, making it easier to capture complex features. Experimental results show that the proposed method achieves high levels of accuracy, recall, precision, F1 More >

Iman S. Al-Mahdi¹, Saad M. Darwish^1,*, Magda M. Madbouly²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 875-909, 2025, DOI:10.32604/cmes.2025.061623 - 11 April 2025

Abstract Heart disease prediction is a critical issue in healthcare, where accurate early diagnosis can save lives and reduce healthcare costs. The problem is inherently complex due to the high dimensionality of medical data, irrelevant or redundant features, and the variability in risk factors such as age, lifestyle, and medical history. These challenges often lead to inefficient and less accurate models. Traditional prediction methodologies face limitations in effectively handling large feature sets and optimizing classification performance, which can result in overfitting poor generalization, and high computational cost. This work proposes a novel classification model for heart… More >

Vatche Bahudian, John Valdovinos^*

Congenital Heart Disease, Vol.20, No.1, pp. 115-127, 2025, DOI:10.32604/chd.2025.063991 - 18 March 2025

Abstract Background: The population of Fontan patients, patients born with a single functioning ventricle, is growing. There is a growing need to develop algorithms for this population that can predict health outcomes. Artificial intelligence models predicting short-term and long-term health outcomes for patients with the Fontan circulation are needed. Generative adversarial networks (GANs) provide a solution for generating realistic and useful synthetic data that can be used to train such models. Methods: Despite their promise, GANs have not been widely adopted in the congenital heart disease research community due, in some part, to a lack of knowledge… More >

Roberto Noya Galluzzo¹, Karine Souza Da Correggio¹, Aldo von Wangenheim², Heron Werner³, Nathalie Jeanne Bravo-Valenzuela⁴, Edward Araujo Júnior^5,6,*, Alexandre Sherlley Casimiro Onofre⁷

Congenital Heart Disease, Vol.20, No.1, pp. 89-101, 2025, DOI:10.32604/chd.2025.063014 - 18 March 2025

Abstract Introduction: Diabetes mellitus (DM), a metabolic disorder, leads to organ damage due to chronic hyperglycemia with multiple pathogenic processes. Gestational diabetes mellitus (GDM) poses risks to mothers and offspring, increasing the incidence of structural congenital heart disease (CHD) and myocardial hypertrophy in newborns. Objective: This review aimed to examine the association between maternal diabetes mellitus and CHD. Methods: This systematic review used the STROBE and TRIPOD checklists registered in PROSPERO (CRD42024513858). It focused on diagnostic test accuracy using the Munn et al. protocol for systematic assessment, emphasizing the “PIRD”: Population, Index Test, Reference Test, Diagnosis of Interest.… More >

John J. Araujo^1,2,*

Congenital Heart Disease, Vol.20, No.1, pp. 61-75, 2025, DOI:10.32604/chd.2025.062927 - 18 March 2025

Abstract Today, more than 90% of children who are born with congenital heart disease survive and reach adulthood, especially in developed countries. Consequently, the population of adults with congenital heart disease has increased significantly over the last few decades. In Latin America and the Caribbean countries, this same scenario is occurring at an accelerated pace. Loss to follow-up is a global problem in adults with congenital heart disease, ranging from 30–60%. In Latin America and Caribbean countries, it is estimated that less than 10% of adults with congenital heart disease are being followed. The small number More >

Ruhul Amin¹, Md. Jamil Khan¹, Tonway Deb Nath¹, Md. Shamim Reza², Jungpil Shin^3,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3907-3919, 2025, DOI:10.32604/cmc.2025.061645 - 06 March 2025

Abstract Heart disease includes a multiplicity of medical conditions that affect the structure, blood vessels, and general operation of the heart. Numerous researchers have made progress in correcting and predicting early heart disease, but more remains to be accomplished. The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches. By using data fusion from several regions of the country, we intend to increase the accuracy of heart disease prediction. A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern… More >

Kassymbek Ozhikenov¹, Zhadyra Alimbayeva^1,*, Chingiz Alimbayev^1,2,*, Aiman Ozhikenova¹, Yeldos Altay¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5257-5284, 2025, DOI:10.32604/cmc.2025.061508 - 06 March 2025

Abstract Myocardial infarction (MI) is one of the leading causes of death globally among cardiovascular diseases, necessitating modern and accurate diagnostics for cardiac patient conditions. Among the available functional diagnostic methods, electrocardiography (ECG) is particularly well-known for its ability to detect MI. However, confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice. This study, therefore, proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI. In particular, the learning vector quantization (LVQ) algorithm was applied, considering the contribution… More >