Collaboration among artificial intelligence (AI) cardiology experts
Cardiovascular medicine doctors and scientists at Mayo Clinic are combining AI with clinical practice, such as with electrocardiogram (ECG or EKG) machine learning, to improve care.
People who receive heart care from Mayo Clinic's Department of Cardiovascular Medicine benefit from access to the clinic's leading-edge research and expertise in artificial intelligence (AI) cardiology to improve clinical care.
AI, which is intelligence exhibited by machines, touches almost every facet of modern life, including medicine. AI is being used at Mayo Clinic to program computers to process and respond to data quickly and consistently for better treatment outcomes. Uses include detecting heart disease, treating strokes faster and enhancing diagnostic radiology capabilities. For example, a Mayo Clinic study applied AI techniques to a new screening tool for left ventricular dysfunction in people without noticeable symptoms. The AI-assisted screening tool identified people at risk of left ventricular dysfunction 93% of the time. To put that in perspective, a mammogram is accurate 85% of the time.
These technologies complement the knowledge of doctors. Ideally, by bringing together direct care and data analysis, AI cardiology allows doctors to spend more time with their patients and improves the shared decision-making process.
Mayo Clinic is a leader in the movement to bring AI tools and technology into clinical practice to benefit people with heart disease and those who are at increased risk of it. The clinic's AI cardiovascular team is applying these new techniques to early risk prediction and diagnosis of serious and complex heart problems.
Artificial intelligence is the ability to make computers or machines learn to solve problems that would otherwise require human effort. Advances in computing power have made it possible to analyze large amounts of data quickly with consistency and accuracy. This has enabled health care scientists to apply AI to huge, complex data sets in a way that improves decision-making, diagnosis and treatment by detecting patterns in patient data.
The basic building block of an AI system is a "neural network." For example, a computer system is trained by ingesting and analyzing hundreds of thousands of sets of similar readings. It becomes experienced in looking at a focused problem, such as ECGs. The result is that an AI system can read a simple test, detect a heart condition and predict possible future problems.
Mayo Clinic leaders have identified several areas of opportunity for AI in health care. The clinic is well situated to advance AI because its long history of high-volume patient care has generated a massive database of historical genomes, microbiomes, ECGs, diagnostic images and other test results. That coupled with the clinic's strong culture of close collaboration among medical doctors, engineers and scientists is driving AI into health care in meaningful ways.
For a deeper look at computer neural networks and deep learning (strong AI), read here.
From research to clinical practice
Cardiovascular medicine doctors and scientists at Mayo Clinic are combining AI with clinical practice for better care. Here are three examples that have moved from the research stage to use in the clinic:
- Helping people who have had a stroke. In emergency rooms, when people come in with a stroke called an intracerebral hemorrhage, they get a CT scan. That scan is examined by a computer trained to analyze CT data, cutting the time to diagnosis and limiting brain damage.
- Preventing heart problems. Applying AI to ECGs has resulted in a low-cost test that can be widely used to detect the presence of a weak heart pump, which can lead to heart failure if left untreated. Mayo Clinic is well situated to advance this use of AI because it has a database of more than 7 million ECGs. First, all identifying patient information is removed to protect privacy. Then this data can be mined to accurately predict heart failure noninvasively, inexpensively and within seconds.
- Detecting atrial fibrillation (a-fib) sooner. AI-guided ECGs are also used to detect faulty heart rhythms (atrial fibrillation) before any symptoms are evident.
Innovation through collaboration
Consulting in the Electrophysiology Laboratory
A doctor (right) works with an Electrophysiology Laboratory colleague to provide exactly the care each person needs.
A collective effort of experts is driving the rapidly maturing field of artificial intelligence in health care. At Mayo Clinic, several medical and surgical specialties, including cardiovascular medicine, neurology, oncology and radiology, have validated approaches to improve clinical care. These advances are shared in the medical literature so that they can be adopted widely to benefit people everywhere.
These AI tools and techniques also play an important role in education and are used by Mayo Clinic's medical students, residents, fellows and experienced surgeons learning new or uncommon procedures. Mayo Clinic leads by holding artificial intelligence symposiums that bring together doctors and scientists to advance this science in health care.
Research innovations in cardiovascular artificial intelligence
The Mayo Clinic cardiovascular medicine team was among the first specialties to rapidly develop and validate these new AI tools and technologies. Possible future uses still in development at Mayo Clinic include:
- Predicting risk early in conditions such as embolic stroke
- Monitoring the heart and detecting arrhythmia in smart clothing projects
- Developing AI technology compatible with smartphones and high-tech stethoscopes
Mayo Clinic physicians, scientists and engineers continually advance the study and practice of artificial intelligence that improves health care. See an article on artificial intelligence at Mayo Clinic here.
See a list of publications about cardiovascular AI by Mayo Clinic researchers on PubMed, a service of the National Library of Medicine.
- Paul A. Friedman, M.D.
- Suraj Kapa, M.D.
- Francisco Lopez-Jimenez, M.D., M.B.A.
- Peter A. Noseworthy, M.D.
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Jan. 14, 2023