A new tool could speed up the diagnosis of cardiovascular diseases. Powered by artificial intelligence (AI), the innovation uses electrocardiograms (ECGs) to diagnose coronary artery disease, myocardial infarction, and congestive heart failure to an accuracy of more than 98.5 percent.
The researchers devised the diagnostic tool by using an AI machine learning algorithm called Gabor-Convolutional Neural Network (Gabor-CNN), which mimics the structure and function of the human brain, enabling computers to learn from past experiences like a human. Using the algorithm, they trained their tool to recognize patterns in patients’ ECGs by inputting examples of ECG signals that reflect cardiovascular diseases.
The diagnostic tool is the first to use GaborCNN to allow for ECG signals to be directly entered into the system for analysis and could lead to advancements in the pursuit of merging AI with medical solutions. The proposed system is equipped to be validated with a bigger database and has the potential to aid clinicians in screening for cardiovascular diseases using ECGs. In a pilot study, the researchers used the tool to analyze ECG signals from 92 healthy individuals, as well as seven patients with coronary artery disease, 148 patients with myocardial infarction, and 15 patients with congestive heart failure.
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