Atrial Fibrillation Could be predicted by Artificial intelligence

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Atrial fibrillation (A-fib) is an abnormal and typically highly fast heart rhythm (arrhythmia) that may result in blood clots in the heart. A-fib raises the risk of stroke, heart failure, and other cardiac issues.

According to the central of diseases control and prevention, It is estimated that 12.1 million people in the United States will have AFib in 2030.

Massachusetts General Hospital, the Broad Institute of MIT, and Harvard's researchers have developed an artificial intelligence tool that may predict individuals who may be at risk of developing AF, according to a study published in Circulation.

Treatments and interventions that may save a person's life are more likely to be implemented sooner rather than later. Based on the data of 45,770 electrocardiograms, the researchers created an artificial intelligence-based strategy to estimate the risk of atrial fibrillation over the following five years. As a follow-up, the researchers applied their approach to three massive datasets, totaling 83,162.

In addition to identifying atrial fibrillation risk, the AI-based technique proved synergistic when paired with established clinical risk factors for predicting the condition. The method was also highly predictive in subgroups of people, such as those who had previously suffered from heart failure or stroke.

Steven A. Lubitz, MD, MPH, a cardiac electrophysiologist at MGH and associate member of the Broad Institute, says, "We see a potential for electrocardiogram-based artificial intelligence algorithms to aid with the identification of those who are most at risk for atrial fibrillation."

"The adoption of such algorithms might motivate doctors to adjust major risk factors for atrial fibrillation that may minimize the likelihood of getting the condition completely," says co-lead author Shaan Khurshid, MD, MPH, electrophysiology clinical and research fellow at MGH.

As a pre-screening tool for individuals who may now be suffering undiscovered atrial fibrillation, the algorithm might urge doctors to look for atrial fibrillation using longer-term cardiac rhythm monitoring, which could lead to stroke preventive interventions.

Using machine learning, this research shows how artificial intelligence (AI) may improve medicine in the future. When it comes to enhancing cardiology care.

"machine learning is poised to make great strides with the explosion of data science technologies and the vast amounts of clinical data now available," says co-author Anthony Philippakis, MD, Ph.D., and co-director of the institute's Eric and Wendy Schmidt Center.

Reference:

Electrocardiogram-based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation in circulation, https://doi.org/10.1161/CIRCULATIONAHA.121.057480

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