Artificial Intelligence and Induced Pluripotent Stem Cells in Long QT Syndrome

An Overview

Abstract

This review addresses the innovative roles of artificial intelligence (AI) and induced pluripotent stem cells (iPSCs) in tackling Long QT Syndrome (LQTS), an inherited cardiac channelopathy associated with the risk of potentially life-threatening arrhythmias. LQTS can be classified as either acquired or congenital, with the latter subdivided into several subtypes associated with specific genetic mutations. The accurate diagnosis and subtyping of LQTS are difficult because of overlapping clinical symptoms and complex genetic variability among patients. Traditional diagnostic approaches, relying on electrocardiograms and genetic testing, often lack precision due to phenotype variability and diagnostic subjectivity. AI holds great promise in the differential diagnosis of LQTS subtypes; machine learning algorithms can analyze large datasets to identify subtle patterns within ECG readings, patient histories, and genomic data. The iPSC technology now allows in vitro modeling of LQTS, enabling the development of patient-specific cardiomyocytes and studying diseases. Combining AI-driven predictive models with the use of iPSC-driven research could result in better diagnostics and treatment approaches by personalizing therapies in unique patients. Upcoming developments in AI and gene-editing technologies carry the promise of enhancing real-time monitoring, risk stratification, and potentially novel therapeutic solutions, with the ultimate expectation of improving patient outcomes and opening the way to more effective, patient-centered LQTS management.