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Artificial Intelligence‐Enabled ECG: Physiologic and Pathophysiologic Insights and Implications

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Abstract

Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date—the electrocardiogram (ECG). The application of artificial intelligence‐enabled ECG (AI‐ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI‐ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417‐3424, 2022.

Figure 1. Figure 1. AI‐ECG model performance before and after septal myectomy. Artificial intelligence model performance in a 21‐year‐old woman with obstructive hypertrophic cardiomyopathy (HCM) before (A) and after (B) septal myectomy. Prior to myectomy, the patient had massive septal hypertrophy (30 mm). Adapted, with permission, from Ko WY, et al., 2020 29.
Figure 2. Figure 2. Proposed approach to help better understand and discover physiologic patterns detected by AI‐ECG models. (A) Standard multivariate discrimination using known physiologic parameters. (B) Improved diagnostic power with deep learning/CNN. (C) Using these unknown patterns to discover new physiology.


Figure 1. AI‐ECG model performance before and after septal myectomy. Artificial intelligence model performance in a 21‐year‐old woman with obstructive hypertrophic cardiomyopathy (HCM) before (A) and after (B) septal myectomy. Prior to myectomy, the patient had massive septal hypertrophy (30 mm). Adapted, with permission, from Ko WY, et al., 2020 29.


Figure 2. Proposed approach to help better understand and discover physiologic patterns detected by AI‐ECG models. (A) Standard multivariate discrimination using known physiologic parameters. (B) Improved diagnostic power with deep learning/CNN. (C) Using these unknown patterns to discover new physiology.
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Anthony H. Kashou, Demilade A. Adedinsewo, Konstantinos C. Siontis, Peter A. Noseworthy. Artificial Intelligence‐Enabled ECG: Physiologic and Pathophysiologic Insights and Implications. Compr Physiol 2022, 12: 3417-3424. doi: 10.1002/cphy.c210001