Neural Network – ABC CARDIOL

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Editorial

Applicability of Machine Learning Algorithms in the Diagnosis of Arrhythmias – How Long Until The Machine Starts Teaching Us?

The electrocardiogram (ECG), in clinical use since the early 1900s, remains a beloved tool for cardiologists, clinicians, and medical students. It is arguably the most important bedside instrument in cardiology, essential for managing arrhythmias and acute ischemic syndromes, as well as guiding electrophysiologic care. Still, advances in electrocardiography interpretation have been only modest in the last hundred years. Devices have become more portable, digital, and capable of longer recordings, but core interpretive principles remain rooted in early 20th-century methods. In recent years, this has been reshaped by the rise of computational technologies, especially artificial intelligence (AI). Tools such as deep neural networks (DNNs) and machine learning models (MLMs) are enabling a new era in ECG analysis, the AI-enhanced electrocardiography (AI-enhanced ECG).

In this issue of the ABC Cardiol, Guimarães do Nascimento et al. report on a systematic review of studies evaluating the applicability of AI-enhanced ECG in the diagnosis of cardiac arrhythmias. They included studies of different methodologies that addressed the role of AI-enhanced ECG in the detection of long-QT syndrome (LQTS) (one study), corrected QT interval (cQTi) (one study), and atrial fibrillation (AF) (eleven studies).

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Original Article

Applicability of Machine Learning Algorithms in Diagnosis of Atrial Fibrillation and LQTS by Electrocardiogram Interpretation: A Systematic Review

Paulo Cainan Guimarães do Nascimento et al. Matthews Silva Martins Alex Cleber Improta-Caria Roque Aras Junior

Abstract

Background

Machine Learning (ML) is a type of algorithm that autonomously learns to recognize complex patterns. In the diagnostic context of cardiac arrhythmias, these algorithms have shown significant advancements due to their ability to provide automated interpretation and pattern recognition in electrocardiograms (ECGs).

Objective

To analyze and identify the applicability, validity, and feasibility of ML algorithm models in the diagnostic process of cardiac arrhythmias through automated electrocardiogram interpretation.

Methods

This systematic literature review was reported according to the PRISMA guidelines. The searches were conducted in the Cochrane Library, EMBASE, LILACS, and PubMed between February 2022 and November 2022. The study period encompasses articles published between 2017 and 2022.

Results

The database search yielded 119 results, covering three subthemes: Long QT Syndrome (LQTS), corrected QT interval (QTc), and atrial fibrillation (AF). AF was the most prevalent theme. The sample sizes were quite variable. The outcomes were mostly satisfactory. In the diagnosis of LQTS using Artificial Intelligence (AI), the algorithm outperformed conventional methods in diagnostic distinction. In the evaluation of QTc, there was no difference between the AI-integrated ECG and the conventional ECG. In the diagnosis of AF, the algorithms, models, and devices demonstrated high sensitivity and specificity, along with greater accuracy.

Conclusion

ML models in the diagnostic process of cardiac arrhythmias are feasible and rapidly developing. They demonstrate accuracy values between 96.4% and 98.2%, sensitivity between 92.8% and 99.4%, and specificity between 95% and 98.1%, particularly in the diagnosis of atrial fibrillation.