Defibrillators are used to deliver electrical current to the heart as a treatment for cardiac arrest that can be potentially fatal. AI has a major impact on how defibrillators can work more efficiently, with machine learning algorithms becoming more accurate with life-saving treatments, according to a recent paper.
Automated external defibrillators (AEDs) and implantable cardioverter-defibrillators (ICDs) use shock advisory algorithms to distinguish echocardiogram traces. The data determines whether rhythms are considered “shockable” or “non-shockable” to decide whether defibrillation is necessary for treatment.
AI can also be used to diagnose the causes of heart attacks, classify heart rhythms without interrupting cardiopulmonary resuscitation (CPR), and predict defibrillation success, according to “Role of artificial intelligence in defibrillators: a narrative review,” by researchers from the UK hospitals and universities.
While the success rate has improved, concerns about cost and high processing power remain a challenge.
How machine learning is implemented in medical applications has evolved in recent years. Currently, supervised machine learning models are still needed for defibrillator applications. Deep learning replicates the brain’s neural networks with artificial neural networks (ANN), which contain layers of nodes that process input data.
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