Artificial intelligence-derived electrocardiographic aging and risk of atrial fibrillation: a multi-national study.

Seunghoon Cho, Sujeong Eom, Daehoon Kim, Tae-Hoon Kim, Jae-Sun Uhm, Hui-Nam Pak, Moon-Hyoung Lee, Pil-Sung Yang, Eunjung Lee, Zachi Itzhak Attia, Paul Andrew Friedman, Seng Chan You, Hee Tae Yu, Boyoung Joung

Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea. · pubmed

Paper

Abstract

Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronological age, termed electrocardiographic aging (ECG aging), is associated with atrial fibrillation (AF) risk.

The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.

Research summary

The paper investigates the relationship between AI-derived electrocardiographic aging and the risk of atrial fibrillation, which touches on the concept of aging as it relates to cardiovascular health. While it does not directly address the root causes of aging or lifespan extension, it provides insights into how aging can be assessed through AI in a clinical context, which could have implications for age-related diseases. However, the findings appear to be more incremental rather than groundbreaking, limiting its overall impact.

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