Speakers: Amit Noheria, MBBS, SM

Amit Noheria, MBBS, SM

Director, Program for AI & Research in Cardiovascular Medicine
Associate Professor of Medicine, Cardiovascular Electrophysiology
The University of Kansas Medical Center


Dr. Noheria is the founding director of Program for AI Research in Cardiovascular Medicine and an associate professor in the Department of Cardiovascular Medicine in the section of Cardiovascular Electrophysiology. He graduated medical school from the All India Institute of Medical Sciences in New Delhi and earned a Master of Science in Epidemiology at the Harvard School of Public Health in Boston. He finished his clinical training with internal medicine residency at Mayo Clinic in Minnesota, cardiology fellowship at Cedars-Sinai in Los Angeles and electrophysiology fellowship at Mayo Clinic, Minnesota. Before joining KU Medical Center, he was an assistant professor at Washington University in St. Louis.

Dr. Noheria takes care of patients at The University of Kansas Health System who have complex heart rhythm disorders, including congenital heart disease and heart failure. He is passionate about teaching medical students, residents and fellows. He is committed to meaningful research and innovation that can improve the care and lives of patients. His research interests have evolved toward novel processing methods including AI in reexploring electrocardiography (ECG), cardiac resynchronization therapy (CRT) and other aspects of cardiovascular care including arrhythmia ablation and implantable cardiac electronic devices.

AI interpretation of ECGs

Electrocardiography (ECG) interpretation is undergoing a revolution with the advent of artificial intelligence (AI). AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population. AI can be applied to the standard 12-lead resting ECG. This can be extended to single-lead ECGs in external monitors, implantable devices, and direct-to-consumer smart devices. Rhythm classification is the basic application of AI ECG. Additionally, AI ECG algorithms are being developed for screening structural heart disease including hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, pulmonary hypertension, and left ventricular systolic dysfunction. Going beyond, AI algorithms can predict future events like development of systolic heart failure and atrial fibrillation.

AI-ECG exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality. Multiple AI algorithms have already received FDA clearance for rhythm classification in the domain of cardiac monitors and smart watches while others have received breakthrough device designation for identification of cardiac amyloidosis, pulmonary hypertension and left ventricular dysfunction. This article summarizes the current state of the literature on AI ECG to identify cardiac arrhythmias, structural abnormalities and beyond.