AI Model Revolutionizes Early Detection of Cardiac Amyloidosis

AI tool outperforms existing methods in diagnosing cardiac amyloidosis

University of Chicago Medical CenterJul 9 2025 In a new study published in the European Heart Journal, researchers reported the successful development and validation of a medical artificial intelligence (AI) model that screens for cardiac amyloidosis, a progressive and irreversible type of heart disease. The results showed that the AI tool is highly accurate, outperforming existing methods and potentially enabling earlier, more accurate diagnoses so patients can benefit from getting the right treatment sooner. What is cardiac amyloidosis? Cardiac amyloidosis is a heart condition in which abnormal proteins build up in the heart muscle, making it stiff and impairing its ability to pump blood. Multiple life-prolonging drug treatments for this condition have recently become available, but without early diagnosis, physicians miss out on opportunities to extend patients' survival and quality of life. Unfortunately, cardiac amyloidosis can be challenging to diagnose, because it's often difficult to distinguish from other heart issues without a burdensome amount of testing." Jeremy Slivnick, MD, co-lead author, cardiologist, University of Chicago Medicine Developing AI for cardiology The AI model was developed by researchers at the Mayo Clinic and Ultromics, Ltd., an AI echocardiography company. They trained a neural network to detect cardiac amyloidosis using routine heart ultrasound images, known as echocardiograms. The resulting AI model can analyze a single echocardiogram video of the heart's apical four-chamber view to quickly detect cardiac amyloidosis and differentiate it from other similar heart conditions. UChicago Medicine joined 17 other hospitals worldwide to validate and test the algorithm's results in a large and multiethnic patient population. They found that the AI tool demonstrated an accuracy rate of 85% for correctly identifying patients with cardiac amyloidosis and 93% for correctly ruling it out. This efficacy held true across multiple types of cardiac amyloidosis in diverse populations. In their analysis, Slivnick and his colleagues compared the AI model to existing clinical scoring methods commonly used to detect cardiac amyloidosis. Their results showed that it significantly outperformed these traditional approaches, making it easier for doctors to decide who needs advanced imaging tests or further evaluation. "It was exciting to confirm that artificial intelligence can give clinicians reliable information to augment their expert decision-making process," Slivnick said. "Since the new treatments for cardiac amyloidosis are most effective in early stages of the disease, it's critical that we leverage every tool at our disposal to diagnose it as soon as possible." Bringing AI into the clinic The AI model is FDA-cleared and already being implemented at multiple hospitals across the country, and the researchers hope its use will ultimately become widespread in routine cardiac care. "This AI model provides a practical solution," Slivnick said. "Because it automatically analyzes a common echocardiogram view, it can easily integrate into everyday clinical practice without causing hassle or sacrificing diagnostic accuracy." University of Chicago Medical Center Journal reference: Slivnick, J. A., et al. (2025). Cardiac amyloidosis detection from a single echocardiographic video clip: a novel artificial intelligence-based screening tool. European Heart Journal. doi.org/10.1093/eurheartj/ehaf387.

AI-enhanced echocardiography improves early detection of amyloid buildup in the heart

An artificial intelligence (AI) model developed by Mayo Clinic and Ultromics, Ltd., an AI echocardiography company based in Oxford, England, is highly accurate in screening for cardiac amyloidosis, a rare and progressive type of heart failure, according to a new study. The model is the first and only AI tool of its kind. Researchers from Mayo Clinic and Ultromics, with investigators at the University of Chicago Medicine and collaborators around the world, validated and tested the model on a large and multiethnic patient population and compared its abilities to other diagnostic methods for cardiac amyloidosis. Their findings, published in the European Heart Journal, show that the AI model was highly accurate, with 85% sensitivity (correctly identifying those with the disease) and 93% specificity (correctly identifying those without the disease). Using a single echocardiography videoclip, the model was effective across all major types of cardiac amyloidosis and distinguished it from other conditions with similar characteristics. Cardiac amyloidosis is a life-threatening condition where an abnormal protein, called amyloid, builds up in the heart, causing it to stiffen and not work properly. It is often missed because the symptoms and imaging features can be similar to other heart conditions. However, early diagnosis is crucial because new drug therapies are now available that can slow or stop the disease's progression. This work builds on the previous experience of Mayo Clinic and Ultromics in developing an AI echocardiography model to detect heart failure with preserved ejection fraction (HFpEF), which received Food and Drug Administration (FDA) clearance in 2022. HFpEF is a common type of heart failure, associated with high morbidity and mortality, but can be challenging to diagnose. An estimated 15% of patients with HFpEF have cardiac amyloidosis. "This AI model is a breakthrough tool that can help us identify patients earlier so they can receive the treatment they need," says Patricia Pellikka, M.D., a cardiologist at Mayo Clinic and past director of the Mayo Clinic Echocardiography Lab in Rochester. "We found that AI performed better than traditional clinical and transthoracic echo-based screening methods, providing clinicians with stronger insights on which to base decisions for further confirmation tests. New treatments are available for cardiac amyloidosis but are most effective if administered early in the course of the disease." Dr. Pellikka is senior author of the study. The amyloid AI model is FDA-cleared and is currently being used at multiple centers in the U.S. Dr. Pellikka says she looks forward to applying this technology in the clinical practice at Mayo Clinic.

AI Tool Helps Improve Detection of Cardiac Amyloidosis | Newswise

Newswise -- In a new study published in the European Heart Journal, researchers reported the successful development and validation of a medical artificial intelligence (AI) model that screens for cardiac amyloidosis, a progressive and irreversible type of heart disease. The results showed that the AI tool is highly accurate, outperforming existing methods and potentially enabling earlier, more accurate diagnoses so patients can benefit from getting the right treatment sooner. What is cardiac amyloidosis? Cardiac amyloidosis is a heart condition in which abnormal proteins build up in the heart muscle, making it stiff and impairing its ability to pump blood. Multiple life-prolonging drug treatments for this condition have recently become available, but without early diagnosis, physicians miss out on opportunities to extend patients' survival and quality of life. "Unfortunately, cardiac amyloidosis can be challenging to diagnose, because it's often difficult to distinguish from other heart issues without a burdensome amount of testing," explained co-lead author Jeremy Slivnick, MD, a cardiologist at the University of Chicago Medicine. Developing AI for cardiology The AI model was developed by researchers at the Mayo Clinic and Ultromics, Ltd., an AI echocardiography company. They trained a neural network to detect cardiac amyloidosis using routine heart ultrasound images, known as echocardiograms. The resulting AI model can analyze a single echocardiogram video of the heart's apical four-chamber view to quickly detect cardiac amyloidosis and differentiate it from other similar heart conditions. UChicago Medicine joined 17 other hospitals worldwide to validate and test the algorithm's results in a large and multiethnic patient population. They found that the AI tool demonstrated an accuracy rate of 85% for correctly identifying patients with cardiac amyloidosis and 93% for correctly ruling it out. This efficacy held true across multiple types of cardiac amyloidosis in diverse populations. In their analysis, Slivnick and his colleagues compared the AI model to existing clinical scoring methods commonly used to detect cardiac amyloidosis. Their results showed that it significantly outperformed these traditional approaches, making it easier for doctors to decide who needs advanced imaging tests or further evaluation. "It was exciting to confirm that artificial intelligence can give clinicians reliable information to augment their expert decision-making process," Slivnick said. "Since the new treatments for cardiac amyloidosis are most effective in early stages of the disease, it's critical that we leverage every tool at our disposal to diagnose it as soon as possible." Bringing AI into the clinic The AI model is FDA-cleared and already being implemented at multiple hospitals across the country, and the researchers hope its use will ultimately become widespread in routine cardiac care. "This AI model provides a practical solution," Slivnick said. "Because it automatically analyzes a common echocardiogram view, it can easily integrate into everyday clinical practice without causing hassle or sacrificing diagnostic accuracy." "Cardiac amyloidosis detection from a single echocardiographic video clip: a novel artificial intelligence-based screening tool" was published in the European Heart Journal in July 2025. Authors include Jeremy Slivnick, Will Hawkes, Jorge Oliveira, Gary Woodward, Ashley Akerman, Alberto Gomez, Izhan Hamza, Viral Desai, Zachary Barrett-O'Keefe, Martha Grogan, Angela Dispenzieri, Christopher Scott, Halley Davison, Juan Cotella, Matthew Maurer, Stephen Helmke, Marielle Scherrer-Crosbie, Marwa Soltani, Akash Goyal, Karolina Zareba, Richard Cheng, James Kirkpatrick, Tetsuji Kitano, Masaaki Takeuchi, Viviane Tiemi Hotta, Marcelo Luiz Campos Vieira, Pablo Elissamburu, Ricardo Ronderos, Aldo Prado, Efstratios Koutroumpakis, Anita Deswal, Amit Pursnani, Nitasha Sarswat, Amit Patel, Karima Addetia, Frederick Ruberg, Michael Randazzo, Federico Asch, Jamie O'Driscoll, Nora Al-Roub, Jordan Strom, Liam Kidd, Sarah Cuddy, Ross Upton, Roberto Lang and Patricia Pellikka.

AI-Enhanced Echocardiography Improves Early Detection of Amyloid Buildup in the Heart | Newswise

Newswise -- ROCHESTER, Minn. -- An artificial intelligence (AI) model developed by Mayo Clinic and Ultromics, Ltd., an AI echocardiography company based in Oxford, England, is highly accurate in screening for cardiac amyloidosis, a rare and progressive type of heart failure, according to a new study. The model is the first and only AI tool of its kind. Researchers from Mayo Clinic and Ultromics, with investigators at the University of Chicago Medicine and collaborators around the world, validated and tested the model on a large and multiethnic patient population and compared its abilities to other diagnostic methods for cardiac amyloidosis. Their findings, published in the European Heart Journal, show that the AI model was highly accurate, with 85% sensitivity (correctly identifying those with the disease) and 93% specificity (correctly identifying those without the disease). Using a single echocardiography videoclip, the model was effective across all major types of cardiac amyloidosis and distinguished it from other conditions with similar characteristics. Cardiac amyloidosis is a life-threatening condition where an abnormal protein, called amyloid, builds up in the heart, causing it to stiffen and not work properly. It is often missed because the symptoms and imaging features can be similar to other heart conditions. However, early diagnosis is crucial because new drug therapies are now available that can slow or stop the disease's progression. This work builds on the previous experience of Mayo Clinic and Ultromics in developing an AI echocardiography model to detect heart failure with preserved ejection fraction (HFpEF), which received Food and Drug Administration (FDA) clearance in 2022. HFpEF is a common type of heart failure, associated with high morbidity and mortality, but can be challenging to diagnose. An estimated 15% of patients with HFpEF have cardiac amyloidosis. "This AI model is a breakthrough tool that can help us identify patients earlier so they can receive the treatment they need," says Patricia Pellikka, M.D., a cardiologist at Mayo Clinic and past director of the Mayo Clinic Echocardiography Lab in Rochester. "We found that AI performed better than traditional clinical and transthoracic echo-based screening methods, providing clinicians with stronger insights on which to base decisions for further confirmation tests. New treatments are available for cardiac amyloidosis but are most effective if administered early in the course of the disease." Dr. Pellikka is senior author of the study. The amyloid AI model is FDA-cleared and is currently being used at multiple centers in the U.S. Dr. Pellikka says she looks forward to applying this technology in the clinical practice at Mayo Clinic. This study was partially supported by a grant from Ultromics and Dr. Pellikka is supported as the Betty Knight Scripps-George M. Gura, Jr., M.D. Professor of Cardiovascular Diseases Clinical Research at Mayo Clinic. Mayo Clinic has a financial interest in this technology and will use any revenue it receives to support its not-for-profit mission in patient care, education and research. ### About Mayo Clinic Mayo Clinic is a nonprofit organization committed to innovation in clinical practice, education and research, and providing compassion, expertise and answers to everyone who needs healing. Visit the Mayo Clinic News Network for additional Mayo Clinic news.