[{"innerBlocks":[],"name":"core\/freeform","postId":552,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"Using electrocardiogram data, Mayo Clinic researchers have developed a convolutional neural network that is able to determine physiologic age, a measure of patients\u2019 overall body function and health status.The artificial intelligence accurately determined a patient\u2019s gender with 90 percent accuracy and chronological age group with 72 percent accuracy. However, what was a surprising discovery for researchers was how discrepancies between AI-predicted age and chronological age ended up serving as a physiological measure of health.

While the AI was trained to predict a person\u2019s age and self-reported sex using standard 12-lead ECGs, it also estimated a patient's chronological age as higher after experiencing adverse health situations\u2014such as heart attack, low ejection fraction and coronary artery disease\u2014and lower age if they experienced few or no adverse events.\u201cWhen the convolutional neural network-predicted age exceeds a patient\u2019s actual age by at least 7 years, there is a higher incidence of cardiovascular comorbidities, potentially suggesting that the convolutional neural network-predicted age from 12-lead ECGs may correlate with physiological health,\u201d according to a study<\/a>, published on Tuesday in the journal Circulation: Arrhythmia and Electrophysiology<\/i>.<\/b>Also See: <\/b>AI predicts long-term mortality by analyzing chest X-rays<\/a>The study\u2019s authors contend that the ability of an AI algorithm to determine physiological age\u2014with further validation in well-characterized, prospectively followed cohorts\u2014has the potential to serve as a rapid measure of overall health status.\u201cWhile physicians already consider whether a patient \u2018appears [their] stated age\u2019 as part of their baseline physical examination, the ability to more objectively and consistently assess this may impact healthcare on multiple levels,\u201d said study author Suraj Kapa, MD, assistant professor of medicine and director for Augmented and Virtual Reality Innovation at Mayo Clinic.\u201cBeing able to more accurately assess overall health status may help doctors determine which patients they should examine further to determine if there are asymptomatic or currently silent diseases that could benefit from early diagnosis and intervention,\u201d added Kapa. \u201cFor people at large, an AI-enhanced electrocardiogram could better show there may be something going on such as a new health issue or comorbid condition that they were otherwise unaware of.\u201dThe Mayo Clinic study was funded using institutional funds for data collection and statistical analyses.","saveContent":"Using electrocardiogram data, Mayo Clinic researchers have developed a convolutional neural network that is able to determine physiologic age, a measure of patients\u2019 overall body function and health status.The artificial intelligence accurately determined a patient\u2019s gender with 90 percent accuracy and chronological age group with 72 percent accuracy. However, what was a surprising discovery for researchers was how discrepancies between AI-predicted age and chronological age ended up serving as a physiological measure of health.While the AI was trained to predict a person\u2019s age and self-reported sex using standard 12-lead ECGs, it also estimated a patient's chronological age as higher after experiencing adverse health situations\u2014such as heart attack, low ejection fraction and coronary artery disease\u2014and lower age if they experienced few or no adverse events.\u201cWhen the convolutional neural network-predicted age exceeds a patient\u2019s actual age by at least 7 years, there is a higher incidence of cardiovascular comorbidities, potentially suggesting that the convolutional neural network-predicted age from 12-lead ECGs may correlate with physiological health,\u201d according to a study<\/a>, published on Tuesday in the journal Circulation: Arrhythmia and Electrophysiology<\/i>.<\/b>Also See: <\/b>AI predicts long-term mortality by analyzing chest X-rays<\/a>The study\u2019s authors contend that the ability of an AI algorithm to determine physiological age\u2014with further validation in well-characterized, prospectively followed cohorts\u2014has the potential to serve as a rapid measure of overall health status.\u201cWhile physicians already consider whether a patient \u2018appears [their] stated age\u2019 as part of their baseline physical examination, the ability to more objectively and consistently assess this may impact healthcare on multiple levels,\u201d said study author Suraj Kapa, MD, assistant professor of medicine and director for Augmented and Virtual Reality Innovation at Mayo Clinic.\u201cBeing able to more accurately assess overall health status may help doctors determine which patients they should examine further to determine if there are asymptomatic or currently silent diseases that could benefit from early diagnosis and intervention,\u201d added Kapa. \u201cFor people at large, an AI-enhanced electrocardiogram could better show there may be something going on such as a new health issue or comorbid condition that they were otherwise unaware of.\u201dThe Mayo Clinic study was funded using institutional funds for data collection and statistical analyses.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

AI leverages ECG data to predict patient’s overall health status

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