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Cardiovascular diseases

Artificial intelligence for the electrocardiogram

Nature Medicine volume 25, pages 22–23 (2019)Cite this article

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Deep-learning algorithms can be applied to large datasets of electrocardiograms, are capable of identifying abnormal heart rhythms and mechanical dysfunction, and could aid healthcare decisions.

Machine learning is a field of AI and is based on computational statistical algorithms that allow computers to learn directly from data, without being explicitly programmed. Thus, for example, machine-learning techniques have the potential to automatically identify the most important features related to key differences in patient data, that is, disease versus healthy. The potential applications of machine learning in healthcare are vast, including screening, disease detection and classification, patient risk stratification, and optimal therapy selection (Fig. 1). In this issue of Nature Medicine two studies2,3 demonstrate the power of machine learning applied to cardiology. Both studies describe deep-learning algorithms applied to large datasets of electrocardiograms (ECGs), the most widely used and perhaps simplest recordings in the clinic, and indicate that machine learning can be applied to identify heart rhythm abnormalities and mechanical dysfunction.

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Fig. 1: A deep neural network approach for analyzing electrocardiograms.

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Authors and Affiliations

  1. Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK

    Ana Mincholé & Blanca Rodriguez

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  1. Ana Mincholé
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  2. Blanca Rodriguez
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Correspondence to Blanca Rodriguez.

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The authors declare no competing interests.

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Mincholé, A., Rodriguez, B. Artificial intelligence for the electrocardiogram. Nat Med 25, 22–23 (2019). https://doi.org/10.1038/s41591-018-0306-1

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