Role of artificial intelligence in congenital heart disease

Abstract

This mini-review explores the transformative potential of artificial intelligence (AI) in improving the diagnosis, management, and long-term care of congenital heart diseases (CHDs). AI offers significant advancements across the spectrum of CHD care, from prenatal screening to postnatal management and long-term monitoring. Using AI algorithms, enhanced fetal echocardiography, and genetic tests improves prenatal diagnosis and risk stratification. Postnatally, AI revolutionizes diagnostic imaging analysis, providing more accurate and efficient identification of CHD subtypes and severity. Compared with traditional methods, advanced signal processing techniques enable a more precise assessment of hemodynamic parameters. AI-driven decision support systems tailor treatment strategies, thereby optimizing therapeutic interventions and predicting patient outcomes with greater accuracy. This personalized approach leads to better clinical outcomes and reduced morbidity. Furthermore, AI-enabled remote monitoring and wearable devices facilitate ongoing surveillance, thereby enabling early detection of complications and provision of prompt interventions. This continuous monitoring is crucial in the immediate postoperative period and throughout the patient’s life. Despite the immense potential of AI, challenges remain. These include the need for standardized datasets, the development of transparent and understandable AI algorithms, ethical considerations, and seamless integration into existing clinical workflows. Overcoming these obstacles through collaborative data sharing and responsible implementation will unlock the full potential of AI to improve the lives of patients with CHD, ultimately leading to better patient outcomes and improved quality of life.

Keywords: Heart diseases; Congenital abnormalities; Artificial intelligence; Echocardiography; Diagnosis

Core Tip: Artificial intelligence (AI) offers transformative potential for congenital heart disease (CHD) care, affecting diagnosis, management, and long-term monitoring. This study explores the multifaceted applications of AI across the journey of patients with CHD, highlighting key advancements and critical challenges. Prenatally, AI-enhanced fetal echocardiography and genetic testing promise earlier and more accurate diagnosis, allowing for timely intervention. Postnatally, AI-driven image analysis accelerates diagnosis, and advanced signal processing improves hemodynamic assessment. AI-driven decision support systems tailor treatment strategies based on individual patient characteristics. Long-term care benefits from AI-enabled remote monitoring and wearable technologies, facilitating proactive management and early detection of complications. However, realizing the full potential of AI in CHD requires addressing significant limitations. The development of robust, standardized datasets is crucial for training reliable AI models. Furthermore, ensuring the transparency and explainability of AI algorithms is essential for building trust and accountability. Ethical considerations, including data privacy, bias mitigation, and equitable access, must be carefully addressed. Finally, the seamless integration of AI tools into existing clinical workflows is vital for practical implementation and widespread acceptance. Addressing these challenges will pave the way for AI to revolutionize CHD care, achieve better outcomes, and improve the lives of patients and their families.