Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of cur‑ rent ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges.

Background: Early repolarization syndrome (ERS) and Brugada syndrome (BrS) are both J-wave syndromes. Both can involve mutations in the SCN5A gene but may exhibit distinct electrocardiographic (ECG) differences. The aim of this systematic review and meta-analysis is to investigate possible differences in ECG markers between SCN5A-positive patients with ERS and BrS. Methods: PubMed and Embase were searched from their inception to 20 October 2021 for human studies containing the search terms “SCN5A” and “variant” and “early repolarization” or “Brugada”, with no language restrictions.Continuous variables were expressed as mean±standard deviation. PR interval, QRS duration, QTc and heart rate from the included studies were pooled to calculate a mean for each variable amongst BrS and ERS patients. A two-tailed Student’s t test was then performed to for comparisons. Results: A total of 328 studies were identified. After full-text screening, 12 studies met our inclusion criteria and were included in this present study. One hundred and four ERS patients (mean age 30.86±14.45) and 2000 BrS patients (mean age 36.17±11.39) were studied. Our meta-analysis found that ERS patients had shorter QRS duration (90.40±9.97 vs. 114.79±20.10, P = 0.0001) and shorter corrected QT intervals (QTc) with borderline significance (393.63±40.04 vs. 416.82±37.43, P = 0.052). By contrast, no significant differences in baseline heart rate (65.15±18.78 vs. 76.06±18.78, P = 0.068) or PR intervals (197.40±34.69 vs. 191.88±35.08, P = 0.621) were observed between ERS and BrS patients. Conclusion BrS patients with positive SCN5A mutations exhibited prolonged QRS, indicating conduction abnormalities, whereas ERS patients with positive SCN5A mutations showed normal QRS. By contrast, whilst QTc intervals were longer in BrS than in ERS SCN5A positive patients, they were within normal limits. Further studies are needed to examine the implications of these findings for arrhythmic risk stratification.

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques.This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice