Artificial Intelligence and Machine Learning Applications

Abstract

Congenital Heart Disease (CHD) is a multifactorial disorder, which is caused and predisposed by a set of interrelating genetic factors along with epigenetic, environmental, and developmental risk factors. This rich heterogeneity is frequently not reflected in more traditional approaches to risk assessment, diagnosis, and prediction of outcomes. The chapter will look at how artificial intelligence and machine learning will transform the way we know more about the CHD etiology and improve clinical care. We provide an extensive survey of the role of AI and ML methods in key areas that are pertinent to CHD, such as genomic and epigenomic data integration, exposomic and teratogenic risk modeling, advanced cardiac imaging interpretation and dynamic risk stratification. The chapter describes the technical underpinnings of these methods, their contemporary use in prenatal and postnatal care, and how they may benefit precision cardiology through integration of disparate data sources into personalized prediction. Concurrently, we discuss the issues that arise in the potential application of AI in pediatric and perinatal settings, including limited data volumes, model levels of generalizability, interpretability, and ethical concerns as critically. We end by summarising a vision of AI-enabled precision congenital cardiology: where transdisciplinary research, information infrastructures with strong data governance, and transparent algorithmic governance are combined to redefine knowledge about CHD, its diagnosis and management along the lifespan.

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