- Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography‑based diagnosis
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Chung C, Lee S, King E, Liu T, Armoundas A, Bazoukis G, Tse G
- KMID: 2537657
- Int J Arrhythm.
- 2022 Dec;23(4):24.
- doi: 10.1186/s42444-022-00075-x
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,... -
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- Machine learning techniques for arrhythmic risk stratification: a review of the literature
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Chung C, Bazoukis G, Lee S, Liu Y, Liu T, Letsas K, Armoundas A, Tse G
- KMID: 2530965
- Int J Arrhythm.
- 2022 Jun;23(2):10.
- doi: 10.1186/s42444-022-00062-2
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.... -
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- Electrocardiographic features in SCN5A mutation‑positive patients with Brugada and early repolarization syndromes: a systematic review and meta‑analysis
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Radford D, Chou O, Bazoukis G, Letsas K, Liu T, Tse G, Lee S
- KMID: 2534090
- Int J Arrhythm.
- 2022 Sep;23(3):16.
- doi: 10.1186/s42444-022-00066-y
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... -
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