Muhammad Adil¹, Nadeem Javaid^1,*, Imran Ahmed², Abrar Ahmed³, Nabil Alrajeh^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071215 - 10 November 2025

Abstract Heart disease remains a leading cause of mortality worldwide, emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention. However, existing Deep Learning (DL) approaches often face several limitations, including inefficient feature extraction, class imbalance, suboptimal classification performance, and limited interpretability, which collectively hinder their deployment in clinical settings. To address these challenges, we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture. The preprocessing stage involves label encoding and feature scaling. To address the issue of… More >

Congenital Heart Disease, Vol.20, Suppl.1, pp. 1-49, 2025

Abstract This article has no abstract. More >

Shun Maki^1,*, Satoshi Nakano¹, Taiki Haga², Takehiro Niitsu¹, Ikuya Ueta¹

Congenital Heart Disease, Vol.20, No.5, pp. 547-558, 2025, DOI:10.32604/chd.2025.072277 - 30 November 2025

Abstract Background: Considering the limited evidence for acute postoperative nutritional therapy for congenital heart disease (CHD), this study evaluated the effects of achieving enteral nutrition (EN) targets in the acute postoperative phase on clinical outcomes in infants after congenital heart surgery. Methods: This retrospective cohort study, conducted in a multivalent pediatric intensive care unit (PICU), enrolled infants aged ≤6 months following congenital heart surgery between April 2021 and March 2023. Based on the American Society for Parenteral and Enteral Nutrition guidelines, the EN target was defined as two-thirds of the resting energy expenditure with a protein intake… More >

Yang Xu^1,#, Yaqi Zhang^2,#, Meijiao Zhu³, Pengcheng Xue⁴, Siyu Ma¹, Di Yu¹, Liang Hu¹, Yuxi Zhang¹, Wei Peng¹, Jirong Qi¹, Xuyun Wen⁴, Ming Yang³, Xuming Mo^1,2,5,*

Congenital Heart Disease, Vol.20, No.5, pp. 559-570, 2025, DOI:10.32604/chd.2025.072242 - 30 November 2025

Abstract Background: The life-course management of children with tetralogy of Fallot (TOF) has focused on demonstrating brain structural alterations, developmental trajectories, and cognition-related changes that unfold over time. Methods: We introduce an magnetic resonance imaging (MRI) dataset comprising TOF children who underwent brain MRI scanning and cross-sectional neurocognitive follow-up. The dataset includes brain three-dimensional T1-weighted imaging (3D-T1WI), three-dimensional T2-weighted imaging (3D-T2WI), and neurodevelopmental evaluations using the Wechsler Preschool and Primary Scale of Intelligence–Fourth Edition (WPPSI-IV). Results: Thirty-one children with TOF (age range: 4–33 months; 18 males) were recruited and completed corrective surgery at the Children’s Hospital of Nanjing More >

Khaled Elenizi¹, Abdullah Sharaf Aldeen², Nasser Alotaibi^3,*, Nagy Fagir², Hussien Hado², Mubarak Aldossari²

Congenital Heart Disease, Vol.20, No.5, pp. 539-546, 2025, DOI:10.32604/chd.2025.071226 - 30 November 2025

Abstract Persistent left superior vena cava (PLSVC) is a rare congenital anomaly that may complicate cardiac procedures when associated with a dilated coronary sinus (CS) and conduction disturbances. We report the case of a 27-year-old male with Wilson’s disease who presented with complete heart block. Echocardiography showed biatrial enlargement and severe CS dilation, while contrast-enhanced computed tomography (CT) confirmed PLSVC draining into the CS without a bridging vein. Anatomical constraints prevented cardiac resynchronization therapy, and dual-chamber pacemaker implantation proved technically challenging due to lead placement difficulties. This case highlights the importance of thorough preoperative assessment and More >

Min Zeng¹, Fan Yang¹, Jie Huang², Zhongkai Liao², Sheng Liu³, Xu Wang^1,*

Congenital Heart Disease, Vol.20, No.5, pp. 581-589, 2025, DOI:10.32604/chd.2025.070100 - 30 November 2025

Abstract Background: Psychosocial functioning and quality of life (QoL) are strongly associated with outcomes in pediatric heart transplant recipients. The data in pediatric transplantation, however, is limited. This study aims to investigate the associations of perioperative anxiety and depression with postoperative complications, sociodemographic and clinical characteristics. Methods: This observational, analytical, longitudinal study included 42 pediatric participants aged 8 to 16 years old. Preoperative psychological assessments were completed by 36 children, the remaining 6 were unable to participate due to invasive ventilation, extracorporeal membrane oxygenation (ECMO), and physical debilitation. Postoperatively, all 42 subjects completed the psychosocial evaluations. Data… More >

Lanqing Qu^1,2,#, Jinbiao Zhang^1,2,#, Wei Jiang^1,2, Jiayu Zhang^1,2, Die Li¹, Wei Cheng³, Linghua Tao⁴, Hongdan Zhu⁵, Jing Li⁶, Min Xue⁷, Feng Chen⁸, Cuicui Xu⁹, Qiang Shu^1,2,*, Weize Xu^1,2,*

Congenital Heart Disease, Vol.20, No.5, pp. 571-580, 2025, DOI:10.32604/chd.2025.066258 - 30 November 2025

Abstract Background: Congenital heart disease (CHD) is the most common congenital anomaly, but whether the COVID-19 pandemic affects its prevalence is unknown. We aimed to compare the incidence of CHD during the COVID-19 pandemic with that before the pandemic in China. Methods: This multicenter retrospective observational study involved all newborns in seven representative cities of China between 01 September 2019, and 31 December 2021. All the newborns underwent pulse oximetry monitoring combined with cardiac murmur auscultation in the first 6 h to 72 h after birth for CHD screening. We defined fetuses born in and beyond September… More >

Jun He¹, Jianli Luo¹, Yanling Wang^1,*, Dai Zhou^1,*, Shuanglin Xiang^2,*

Congenital Heart Disease, Vol.20, No.5, pp. 613-623, 2025, DOI:10.32604/chd.2025.071773 - 30 November 2025

Abstract Pediatric congenital heart disease (CHD) pharmacotherapy faces three fundamental barriers: developmental pharmacokinetic complexity, anatomic-genetic heterogeneity, and evidence chain gaps. Traditional agents exhibit critical limitations: digoxin’s narrow therapeutic index (0.5–0.9 ng/mL) is exacerbated by ABCB1 mutations (toxicity risk increases 4.1-fold), furosemide efficacy declines by 35% in neonates due to NKCC2 immaturity, and β-blocker responses vary by CYP2D6 polymorphisms (poor metabolizers require 50–75% dose reduction). Novel strategies demonstrate transformative potential—CRISPR editing achieves 81% reversal of BMPR2-associated pulmonary vascular remodeling, metabolically matured cardiac organoids replicate adult myocardial energy metabolism for drug screening, and SGLT2 inhibitors activate triple mechanisms (calcium overload More >

Akbare Yaqub^1,2, Muhammad Sadiq Orakzai², Muhammad Farrukh Qureshi^3,4, Zohaib Mushtaq⁵, Imran Siddique^6,7, Taha Radwan^8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2503-2533, 2025, DOI:10.32604/cmes.2025.071571 - 26 November 2025

Abstract Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, necessitating efficient diagnostic tools. This study develops and validates a deep learning framework for phonocardiogram (PCG) classification, focusing on model generalizability and robustness. Initially, a ResNet-18 model was trained on the PhysioNet 2016 dataset, achieving high accuracy. To assess real-world viability, we conducted extensive external validation on the HLS-CMDS dataset. We performed four key experiments: (1) Fine-tuning the PhysioNet-trained model for binary (Normal/Abnormal) classification on HLS-CMDS, achieving 88% accuracy. (2) Fine-tuning the same model for multi-class classification (Normal, Murmur, Extra Sound, Rhythm Disorder), which yielded… More >

Yazeed Alkhrijah¹, Marwa Fahim², Syed Muhammad Usman³, Qasim Mehmood³, Shehzad Khalid^4,5,*, Mohamad A. Alawad¹, Haya Aldossary⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2339-2355, 2025, DOI:10.32604/cmes.2025.071512 - 26 November 2025

Abstract Atrial Fibrillation (AF) is a cardiac disorder characterized by irregular heart rhythms, typically diagnosed using Electrocardiogram (ECG) signals. In remote regions with limited healthcare personnel, automated AF detection is extremely important. Although recent studies have explored various machine learning and deep learning approaches, challenges such as signal noise and subtle variations between AF and other cardiac rhythms continue to hinder accurate classification. In this study, we propose a novel framework that integrates robust preprocessing, comprehensive feature extraction, and an ensemble classification strategy. In the first step, ECG signals are divided into equal-sized segments using a… More >