Artificial intelligence is the booming topic everywhere across the globe these days. AI has become the most powerful tool in stroke care. Artificial intelligence can help doctors in many ways, boosting the healthcare system. It can speed up detection, improve quality of life for patients, and can reduce risk among those who have experienced a stroke. The most sophisticated and carefully developed stroke AI solutions are providing the healthcare industry with great stroke detection tools that can improve patient outcomes.
An Artificial intelligence-based method for detecting patients who are at high risk for atrial fibrillation has been designed by a team led by researchers at Harvard-affiliated Massachusetts General Hospital and the Broad Institute of MIT and Harvard. When coming to Atrial fibrillation, a rapid heart rate that is common leading to the formation of clots in the heart that can travel to the formation of clots in the heart can also lead to the brain causing a stroke.
The researchers came up with an artificial intelligence based method to anticipate the risk of atrial fibrillation within the next five years based on results from electrocardiograms in 45,770 patients receiving primary care at Massachusetts General Hospital. Later, they applied their technique to three types of large sets of data from studies including a total of 83,162 individuals. The method based on the artificial intelligence can anticipate atrial fibrillation risk factors for predicting atrial fibrillation. This technique was highly predictive in subsets of individuals such as those with prior heart failure or stroke.
A senior author Steven A. Lubitz, who is also a cardiac electro physiologist at Massachusetts General Hospital and an associate member at the Broad Institute, and an associate professor of medicine at Harvard Medical School. “We see a role for electrocardiogram-based artificial intelligence algorithms to assist with the identification of individuals at greatest risk for atrial fibrillation”.
This new algorithm can serve as a form of pre-screening tool for patients who may experience an undetected martial fibrillation, prompting clinicians to search for martial fibrillation using longer-team cardiac rhythm monitors, which can lead to stroke prevention measures.
The findings also demonstrated the power of artificial intelligence that involves a specific type called ML to advance medicine. “With the explosion of data science technologies and the vast amounts of clinical data now available, machine learning is poised to help clinicians and researchers make great strides in enhancing cardiology care,” says co-author Anthony Philippakis, chief data officer at the Broad and co-director of the institute’s Eric and Wendy Schmidt Center. “As a data scientist and former cardiologist, I’m excited to see how machine learning–based methods can work with the tests and clinical approaches we use every day to help us improve risk prediction and take care of patients with atrial fibrillation.”
“The application of such algorithms could prompt clinicians to modify important risk factors for atrial fibrillation that may reduce the risk of developing the disease altogether”, adds another co-lead author and an electrophysiology clinical and research fellow at MGH Shaan Khurshid.
These way artificial intelligence has been driving the healthcare sector to come up with things that which are not possible before. Artificial intelligence has been a major contributing factor for all the developments that are taking place across industries these days.