Brown Grace, Conway Samuel, Ahmad Mahmood, Adegbie Divine, Patel Nishil, Myneni Vidushi, Alradhawi Mohammad, Kumar Niraj, Obaid Daniel R, Pimenta Dominic, Bray Jonathan J H
Cardiology Department, Royal Free Hospital, London, UK
Cardiology Department, Royal Free Hospital, London, UK.
Open Heart. 2022 Jul;9(2). doi: 10.1136/openhrt-2022-001976.
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.
自动体外除颤器（AED）和植入式心脏复律除颤器（ICD）用于治疗危及生命的心律失常。在临床实践中，AED和ICD使用电击建议算法将心电图描记分类为可电击或不可电击节律。最近对机器学习算法进行了评估，以用于电击决策分类，其准确性不断提高。除了单纯的节律分类外，还对它们在心脏骤停原因诊断、除颤成功预测以及无需中断心肺复苏的节律分类方面进行了评估。本综述探讨了机器学习在AED和ICD中的多种应用。虽然这些技术是令人兴奋的研究领域，但它们的广泛应用仍存在局限性，包括高处理能力、成本和“黑匣子”现象。
