Abstract

Background: Congenital heart disease (CHD) is the most prevalent birth defect worldwide, impacting roughly 1 in 100 live births, which equates to over 1.35 million infants annually. Congenital heart disease (CHD) includes a wide range of structural cardiac defects that arise during fetal development and endure throughout an individual’s lifetime. Aim: The Aim is to analyze the extensive spectrum of congenital heart disease (CHD), encompassing its structural, diagnostic, therapeutic, psychosocial, and global health dimensions, while highlighting the disparities between high-income and low and middle-income countries (LMICs). Objectives: 1. To analyze the structural variations of congenital heart defects. 2. To review advancements in prenatal and postnatal diagnostic techniques, including fetal echocardiography and AI-assisted imaging 3. To evaluate evolving treatment modalities such as catheter-based interventions, minimally invasive and robotic surgeries, and novel regenerative therapies like tissue-engineered valves and stem cell treatments. 4. To highlight the lifelong psychological and developmental challenges faced by CHD patients and their families. 5. To assess global disparities in CHD outcomes and propose policy-driven strategies for equitable care. Material: This study integrates multidisciplinary literature, real-world patient accounts, and global health data, bolstered by clinical insights from paediatric cardiology, surgery, imaging, psychology, and health policy domains. Result: In affluent nations, more than 85% of infants diagnosed with congenital heart disease now reach maturity, attributable to advancements in identification and treatment. Nevertheless, low and middle-income countries encounter postponed diagnoses, restricted access to specialized healthcare, and elevated death rates due to infrastructural and institutional deficiencies. The data demonstrates a significant disparity in results that highlights the necessity for focused global actions. Conclusion: Coronary heart disease continues to be a significant clinical and societal concern globally. Addressing the care gap necessitates synchronized global initiatives encompassing early screening, healthcare workforce enhancement, and the incorporation of patient-centered congenital heart disease services into national health frameworks. Advocating for health equity guarantees both survival and quality of life for all children born with congenital heart disease, irrespective of geographic or socioeconomic conditions.

• 1.   Moro, C., Iudici, A., & Turchi, G. P. (2025). Parents of Children with Congenital Heart Disease (CHD): A Narrative Study of the Social and Clinical Impact of CHD Diagnosis on Their Role and Health. Behavioral Sciences, 15(3), 269.
• 2.   Haq, I. U., Husnain, G., Ghadi, Y. Y., Innab, N., Alajmi, M., & Aljuaid, H. (2025). Enhancing pediatric congenital heart disease detection using customized 1d cnn algorithm and phonocardiogram signals. Heliyon, 11(3).
• 3.   Peña-Martínez, E. G. (2025). Global Evaluation of Congenital Heart Disease-Associated Non-Coding Variants (Doctoral dissertation, University of Puerto Rico, Rio Piedras (Puerto Rico)).
• 4.   Zhang, Q., Lai, S., Zhang, Y., Ye, X., Wu, Y., Lin, T., ... & Yan, J. (2023). Associations of elevated glucose levels at each time point during OGTT with fetal congenital heart diseases: a cohort study of 72,236 births. BMC pregnancy and childbirth, 23(1), 837.
• 5.   Khera, R., Asnani, A. H., Krive, J., Addison, D., Zhu, H., Vasbinder, A., ... & Okwuosa, T. M. (2025). Artificial intelligence to enhance precision medicine in cardio-oncology: a scientific statement from the American Heart Association. Circulation: Genomic and Precision Medicine, 18(2), e000097.
• 6.   Makhaddin, G. I., & Atakishiyeva, V. N. (2025). Inflammatory Heart Disease in Patients with Rheumatological. Inflammatory Myocardial Diseases, 3.
• 7.   Gasimova, F. N., Babayeva, G. H., NurMammadova, G. S., Babayeva, L. K., Makhaddin, G. I., Atakishiyeva, V. N., ... & Niftiyev, P. G. (2025). Inflammatory Heart Disease in Patients with Rheumatological Profile. In Inflammatory Myocardial Diseases. IntechOpen.
• 8.   Fan, S., & Hu, Y. (2022). Integrative analyses of biomarkers and pathways for heart failure. BMC Medical Genomics, 15(1), 72.
• 9.   Ji, Y., Zhang, X., Tang, H., Luo, H., Zhao, S., Qiu, Z., ... & Diao, L. (2019, September). Education platform of congenital heart disease based on mixed reality technology. In International Conference of Pioneering Computer Scientists, Engineers and Educators (pp. 313-334). Singapore: Springer Singapore.
• 10.   Kim, D. S. (2016). A computational pipeline for identifying copy number variation from single nucleotide polymorphism data and applications to congenital heart disease (Doctoral dissertation).
• 11.   Soni, T., Gupta, D., Uppal, M., Juneja, S., Gulzar, Y., & Ghafoor, K. Z. (2024). Deep neural network framework for predicting cardiovascular diseases from ECG signals. Recent Advances in Computer Science and Communications.
• 12.   Liu, Y., & Wang, B. (2025). Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review. Frontiers in Public Health, 13, 1510456.
• 13.   Sooriamoorthy, D., Malarvili, M. B., Anas, A., & Meste, O. (2025). Emerging Technologies and Concepts for Cardiovascular Risk Detection.
• 14.   Liu, G., Zhang, Z., Zhang, J., & Gao, H. (2025). Non-coding RNAs: targets for. Reviews in Cardiovascular Pharmacology: 2023.
• 15.   Zhang, X., Liu, Z., Guo, B., Cui, Q., Gao, J., Kang, Z., & Xiao, G. (2024). Pharmacological Mechanisms of Periplocae Cortex Against Congestive Heart Failure Based on Network Pharmacology and Experimental Evaluation. Natural Product Communications, 19(3), 1934578X241229289.
• 16.   Verghese, G. R., Clarke-Myers, K., & Anderson, J. B. (2023). Quality and Value Improvement in Pediatric Cardiac Care: The World of Quality Improvement. In Pediatric Cardiology: Fetal, Pediatric, and Adult Congenital Heart Diseases (pp. 1-39). Cham: Springer International Publishing.
• 17.   Arsalan, M. H., Mubin, O., Al Mahmud, A., Khan, I. A., & Hassan, A. J. (2025, April). Mapping Data-Driven Research Impact Science: The Role of Machine Learning and Artificial Intelligence. In Metrics (Vol. 2, No. 2, p. 5). MDPI.
• 18.   Wu, Z., Cai, R., Wu, S., Zhu, G., Cui, S., Yu, Q., ... & Zhou, Y. (2022). The Meridian–Viscera Correlationship: Theory and Mechanisms of Heart Meridian–Heart–Brain Interactions. In Advanced Acupuncture Research: From Bench to Bedside (pp. 311-344). Cham: Springer International Publishing.
• 19.   He, B., Kwan, A. C., Cho, J. H., Yuan, N., Pollick, C., Shiota, T., ... & Ouyang, D. (2023). Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature, 616(7957), 520-524.
• 20.   Ouyang, D., He, B., Ghorbani, A., Yuan, N., Ebinger, J., Langlotz, C. P., ... & Zou, J. Y. (2020). Video-based AI for beat-to-beat assessment of cardiac function. Nature, 580(7802), 252-256.

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