Key result
Machine learning algorithms in automated external defibrillators and implantable cardioverter defibrillators show increasing accuracy for shock decision classification and other applications.
Why the study?
Machine learning algorithms have recently shown increasing accuracy for shock decision classification and other novel defibrillator applications, prompting a review of their role and limitations.
Do machine learning algorithms improve rhythm classification and outcome prediction in automated external defibrillators and implantable cardioverter defibrillators?
Do machine learning algorithms improve rhythm classification and outcome prediction in automated external defibrillators and implantable cardioverter defibrillators?
Machine learning algorithms show promise in improving the accuracy of rhythm classification and predicting outcomes in AEDs and ICDs, but widespread clinical implementation is currently limited by processing requirements and data generalisability.
Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) are used to treat life-threatening arrhythmias. AEDs and ICDs use shock advice algorithms to classify ECG tracings as shockable or non-shockable rhythms in clinical practice. Machine learning algorithms have recently been assessed for shock decision classification with increasing accuracy. Outside of rhythm classification alone, they have been evaluated in diagnosis of causes of cardiac arrest, prediction of success of defibrillation and rhythm classification without the need to interrupt cardiopulmonary resuscitation. This review explores the many applications of machine learning in AEDs and ICDs. While these technologies are exciting areas of research, there remain limitations to their widespread use including high processing power, cost and the 'black-box' phenomenon.
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Brown et al. (2022) conducted a review in Life-threatening arrhythmias. Machine learning algorithms was evaluated. Machine learning algorithms in automated external defibrillators and implantable cardioverter defibrillators show increasing accuracy for shock decision classification and other applications.
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