Current and Future Use of Artificial Intelligence in Electrocardiography

Manuel Martínez-Sellés, Manuel Marina-Breysse, Manuel Martínez-Sellés, Manuel Marina-Breysse

Abstract

Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.

Keywords: artificial intelligence; cost effectiveness; deep learning; diagnosis; electrocardiography; machine learning; prognosis.

Conflict of interest statement

Manuel Marina-Breysse is a founding member of IDOVEN. ChatGPT was used but only in the last sentence of the conclusion “In the future, artificial intelligence is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed”. This sentence has been copied verbatim. As our focus is artificial intelligence in electrocardiography, we thought it appropriate to keep the exact words obtained by this open artificial intelligence chat. The authors declare no other conflict of interest.

Figures

Figure 1
Figure 1
Supervised and unsupervised learning.
Figure 2
Figure 2
Example of a sensitivity map for understanding the region where the AI algorithm is focusing to predict an output. The most important region is in orange, and the least important regions are in black.
Figure 3
Figure 3
Use of AI ECG analysis for real-time ECG monitoring.
Figure 4
Figure 4
Possible use cases of AI ECG analysis in current clinical workflows.

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