How AI Is Changing the World of Defibrillators

The success rate has improved but the cost and high processing power are concerns

How artificial intelligence is changing the world of defibrillators Success rates have improved, but the cost and high computing power are a problem Defibrillators are used to deliver electrical current to the heart as a treatment for potentially fatal cardiac arrest. Artificial intelligence is having a big impact on how defibrillators work more efficiently, with machine learning algorithms becoming more accurate in life-saving treatments, according to a recent paper. Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) use shock recommendation algorithms to recognize echocardiographic recordings. The data determines whether the rhythms are considered “shockable” or “non-shockable” to decide if defibrillation is necessary for treatment. Artificial intelligence can also be used to diagnose the causes of heart attacks, classify heart rhythms without interfering with cardiopulmonary resuscitation (CPR) and predict the success of defibrillation, according to “Role of Artificial Intelligence in Defibrillators: A Narrative Review” by UK researchers. hospitals and universities. Although success rates have increased, concerns about cost and high computing power remain a concern. The way 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 simulates the brain’s neural networks using artificial neural networks (ANNs), which have layers of nodes that process input data. AI can be used for standard ECG analysis. Convolutional neural networks (CNNs) are a subcategory of ANNs that exploit high-level features from raw data. This method is used in medical imaging and ECG analysis, but can also be used to evaluate multiple dimensions of data sets. In one model, CNN’s ECG interpretation is more accurate than that of human cardiologists, but automated ECG analysis is still not widely used. Smartwatches such as the Apple Watch and other wearable technology are being used more, especially with the ability to perform automated single-lead EKGs to detect atrial fibrillation. In one study, the Kardia Band (KB) algorithm used in the Apple Watch was not as accurate as the client’s diagnosis. The algorithm was unable to interpret more than half of the ECG and CB recordings. A doctor’s supervision is still necessary for the most accurate diagnosis. In addition, AI can be used as a screening for early pulmonary hypertension and asymptomatic left ventricular dysfunction. The Mayo Clinic used data from nearly 45,000 patients to train a CNN to detect asymptomatic left ventricular dysfunction. The results are promising, showing that a positive AI screen predicts a fourfold higher risk of developing ventricular dysfunction than patients who did not use the screen. AI can also judge when to stop CPR and optimize shock delivery. It can also reduce the number of unnecessary shocks and improve the patient’s quality of life. Future plans include creating a robust ECG dataset to build and test technology comparison algorithms.