Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes.

Journal: The Canadian journal of cardiology
Published Date:

Abstract

Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical gaps remain, particularly within case ascertainment, access to genetic testing, and risk stratification. Artificial intelligence (AI), specifically machine learning and its subset deep learning, present promising solutions to these challenges. The capacity of AI to process vast amounts of patient data and identify disease patterns differentiates them from traditional methods, which are time- and resource-intensive. To date, AI models have shown immense potential in condition detection (including asymptomatic/concealed disease) and genotype and phenotype identification, exceeding expert cardiologists in these tasks. Additionally, they have exhibited applicability for general population screening, improving case ascertainment in a set of conditions that are often asymptomatic such as left ventricular dysfunction. Third, models have shown the ability to improve testing protocols; through model identification of disease and genotype, specific clinical testing (eg, drug challenges or further diagnostic imaging) can be avoided, reducing health care expenses, speeding diagnosis, and possibly allowing for more incremental or targeted genetic testing approaches. These significant benefits warrant continued investigation of AI, particularly regarding the development and implementation of clinically applicable screening tools. In this review we summarize key developments in AI, including studies in long QT syndrome, Brugada syndrome, hypertrophic cardiomyopathy, and arrhythmogenic cardiomyopathies, and provide direction for effective future AI implementation in clinical practice.

Authors

  • Sophie Sigfstead
    Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada.
  • River Jiang
    Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada.
  • Robert Avram
    Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, Montreal, QC H1T 1C8, Canada. Electronic address: robert.avram.md@gmail.com.
  • Brianna Davies
    Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
  • Andrew D Krahn
    Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada. Electronic address: akrahn@mail.ubc.ca.
  • Christopher C Cheung
    Division of Cardiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.