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How experts are using AI to better detect sleep apnea, especially in women
Mayo Clinic researchers have developed an artificial intelligence (AI) algorithm that can identify obstructive sleep apnea (OSA) using the results from an electrocardiogram (ECG) -- a common heart test. The innovation could make it faster, cheaper, and easier to spot sleep apnea, particularly in women, who are often underdiagnosed. A common but underrecognized condition OSA affects more than 936 million adults ages 30-69 worldwide and poses significant cardiovascular risks. People with OSA experience repeated episodes of upper airway collapse or blockage during sleep. This collapse causes breathing to stop or become shallow repeatedly, which often leads to loud snoring and gasping. Despite its prevalence, it often goes undiagnosed. "Obstructive sleep apnea or OSA is a highly prevalent disease with important cardiovascular consequences," says Virend Somers, M.D., Ph.D., Alice Sheets Marriott Professor of Cardiovascular Medicine and senior author of the study published in JACC: Advances. "OSA affects the heart to the point where AI algorithms can detect the OSA signature from the ECG, which in essence is a representation of the electrical activity of the heart muscle cells," Dr. Somers adds. AI model shows strong performance -- especially for women In the study, the researchers used AI algorithms to review the 12-lead ECG test results of 11,299 patients at Mayo Clinic who had undergone the test along with sleep evaluations. More than 7,000 of them had a known diagnosis of OSA, and 4,000 were controls. "The most surprising finding was the increased visibility on the ECG of OSA in the females compared to the males, even though the OSA severity was less in the females," says Dr. Somers. "This is relevant since emerging data consistently suggest that females have a greater relative likelihood of suffering the cardiovascular consequences of OSA, even if their OSA may be considered 'milder' by standard diagnostic criteria," he adds. The test also strongly suggests women may suffer more damage to their heart muscle cells from OSA, Dr. Somers says. Dr. Somers underscores that this approach may have the potential to evaluate whether a given OSA treatment may be able to reduce a patient's cardiovascular risk.
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How Mayo Clinic Experts Are Using AI to Better Detect Sleep Apnea, Especially in Women | Newswise
Newswise -- ROCHESTER, Minn. -- Mayo Clinic researchers have developed an artificial intelligence (AI) algorithm that can identify obstructive sleep apnea (OSA) using the results from an electrocardiogram (ECG) - a common heart test. The innovation could make it faster, cheaper, and easier to spot sleep apnea, particularly in women, who are often underdiagnosed. OSA affects more than 936 million adults ages 30-69 worldwide and poses significant cardiovascular risks. People with OSA experience repeated episodes of upper airway collapse or blockage during sleep. This collapse causes breathing to stop or become shallow repeatedly, which often leads to loud snoring and gasping. Despite its prevalence, it often goes undiagnosed. "Obstructive sleep apnea or OSA is a highly prevalent disease with important cardiovascular consequences," says Virend Somers, M.D., Ph.D., Alice Sheets Marriott Professor of Cardiovascular Medicine and senior author of the study published in JACC: Advances. "OSA affects the heart to the point where AI algorithms can detect the OSA signature from the ECG, which in essence is a representation of the electrical activity of the heart muscle cells," Dr. Somers adds. In the study, the researchers used AI algorithms to review the 12-lead electrocardiogram (ECG) test results of 11,299 patients at Mayo Clinic who had undergone the test along with sleep evaluations. More than 7,000 of them had a known diagnosis of OSA, and 4,000 were controls. "The most surprising finding was the increased visibility on the ECG of OSA in the females compared to the males, even though the OSA severity was less in the females," says Dr. Somers. "This is relevant since emerging data consistently suggest that females have a greater relative likelihood of suffering the cardiovascular consequences of OSA, even if their OSA may be considered 'milder' by standard diagnostic criteria," he adds. The test also strongly suggests women may suffer more damage to their heart muscle cells from OSA, Dr. Somers says. Dr. Somers underscores that this approach may have the potential to evaluate whether a given OSA treatment may be able to reduce a patient's cardiovascular risk. 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.
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Mayo Clinic researchers have created an AI algorithm that can identify obstructive sleep apnea using electrocardiogram results, offering a faster and cheaper diagnostic method. The innovation shows particular promise for detecting sleep apnea in women, who are often underdiagnosed despite facing greater cardiovascular risks.
Mayo Clinic researchers have developed a groundbreaking artificial intelligence algorithm that can identify obstructive sleep apnea (OSA) using electrocardiogram (ECG) results, potentially revolutionizing how this common but underdiagnosed condition is detected
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. The innovation promises to make sleep apnea diagnosis faster, cheaper, and more accessible, with particular benefits for women who are frequently underdiagnosed.The AI system analyzes the electrical activity of heart muscle cells captured in standard 12-lead ECG tests to detect the signature patterns of obstructive sleep apnea. "OSA affects the heart to the point where AI algorithms can detect the OSA signature from the ECG, which in essence is a representation of the electrical activity of the heart muscle cells," explains Dr. Virend Somers, Alice Sheets Marriott Professor of Cardiovascular Medicine and senior author of the study published in JACC: Advances
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.Obstructive sleep apnea affects more than 936 million adults ages 30-69 worldwide and poses significant cardiovascular risks
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. The condition involves repeated episodes of upper airway collapse or blockage during sleep, causing breathing to stop or become shallow repeatedly, often accompanied by loud snoring and gasping. Despite its high prevalence, OSA frequently goes undiagnosed, leaving millions of people at risk for serious cardiovascular complications.
Source: Medical Xpress
"Obstructive sleep apnea or OSA is a highly prevalent disease with important cardiovascular consequences," notes Dr. Somers
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. Traditional sleep studies, while effective, can be expensive, time-consuming, and less accessible to many patients, creating barriers to timely diagnosis and treatment.The research team analyzed 12-lead ECG test results from 11,299 patients at Mayo Clinic who had undergone both ECG testing and sleep evaluations. More than 7,000 participants had confirmed OSA diagnoses, while 4,000 served as controls
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.The study's most significant discovery was the enhanced visibility of OSA signatures in women's ECGs compared to men's, despite women typically presenting with less severe symptoms by standard diagnostic criteria. "The most surprising finding was the increased visibility on the ECG of OSA in the females compared to the males, even though the OSA severity was less in the females," Dr. Somers reveals
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.This finding has profound implications for women's cardiovascular health, as emerging research consistently indicates that women face a greater relative likelihood of suffering cardiovascular consequences from OSA, even when their condition appears milder by conventional standards
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The AI algorithm's ability to detect sleep apnea through routine heart tests could transform screening and diagnosis protocols. The technology suggests that women may experience more significant damage to their heart muscle cells from OSA than previously understood, highlighting the critical need for improved detection methods in female patients
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.Beyond diagnosis, Dr. Somers emphasizes that this approach may enable clinicians to evaluate whether specific OSA treatments can effectively reduce a patient's cardiovascular risk, potentially personalizing treatment strategies based on individual cardiac impact patterns
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