Mayo Clinic Pioneers AI Tools for Early Detection in Cardiovascular Medicine
- 2 days ago
- 3 min read
Artificial intelligence (AI) is rapidly moving from the research lab to the clinical setting, revolutionizing how heart doctors and scientists diagnose and treat diseases of the heart and blood vessels. Mayo Clinic is recognized as a leader in this movement, leveraging its massive database of historical genomes, ECGs, and diagnostic images, combined with a strong culture of collaboration among medical doctors, engineers, and scientists. The core benefit of integrating AI into Cardiovascular Medicine is the ability to process and respond to complex data quickly and consistently, driving better treatment outcomes and enhancing Early Detection capabilities.
The adoption of these technologies is already translating into clinical improvements. For example, AI-driven applications are being used on simple electrocardiograms (ECGs) to detect conditions that lack obvious symptoms. One significant achievement involves applying AI to ECGs to develop a low-cost, widely usable test for detecting the presence of a weak heart pump (low ventricular ejection fraction). This condition, if left untreated, can lead to heart failure. In a Mayo Clinic study, the AI-assisted screening tool successfully found people at risk of left ventricular dysfunction 93% of the time, a remarkable accuracy that surpasses that of a mammogram (85%). Furthermore, AI-guided ECGs are effectively used to detect faulty heart rhythms, known as atrial fibrillation (AFib), sooner than traditional methods, often before symptoms become evident. AI is also being used in emergency rooms to analyze CT scans for patients experiencing an intracerebral hemorrhage stroke, cutting down time to diagnosis and limiting potential brain damage.
Beyond common heart conditions, Mayo Clinic is using AI and genetics to uncover hidden, high-risk conditions. Researchers are pioneering early detection methods for arrhythmogenic right ventricular cardiomyopathy (ARVC), a rare genetic heart condition that can lead to severe health consequences, including sudden death, often without early symptoms. This research, involving experts like Dr. Peter Noseworthy and Dr. John Giudicessi, marks a critical shift toward proactive medicine by analyzing genetic and electrical signals of the heart to identify risk factors before significant damage occurs.
Another major innovation addresses blood mutations linked to both cancer and heart disease. Researchers have developed an AI tool called UNISOM (UNIfied SOmatic calling and Machine learning) to identify early signs of clonal hematopoiesis of indeterminate potential, or CHIP. CHIP involves a cluster of mutated blood cells found in about 1 in 5 older adults. While asymptomatic, CHIP raises the risk of leukemia more than 10 times and increases the risk of heart disease up to four times. UNISOM allows clinicians to identify these CHIP-related mutations using standard genetic datasets, detecting small but important changes present in fewer than 5% of blood cells that standard sequencing methods often miss. This translational effort helps guide proactive monitoring and preventive care.
By bringing together direct care and advanced data analysis, AI tools complement the extensive knowledge of doctors, ideally allowing them to spend more time engaging with their patients and improving the shared decision-making process. The ongoing research promises further advancements, with future uses possibly including predicting the risk of conditions like embolic stroke early, and developing AI technology compatible with smartphones, high-tech stethoscopes, and even smart clothing for continuous monitoring. This collaborative, data-driven approach positions Mayo Clinic at the forefront of improving health care globally.
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Keywords: Cardiovascular Medicine
Cardiovascular Medicine
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