Mayo Clinic trial signals potential for AI-guided heart disease detection

梅奥诊所试验信号显示AI引导的心脏病检测的潜力

2021-05-08 03:30:10 Healthcare IT News

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The Mayo Clinic released results of a trial this week that suggests potential for artificial intelligence to assist with early diagnosis of some types of heart disease.   The study, published in Nature Medicine on Thursday, found that an AI-enabled electrocardiogram increased the diagnosis of low ejection fraction.   "The AI-enabled EKG facilitated the diagnosis of patients with low ejection fraction in a real-world setting by identifying people who previously would have slipped through the cracks," said Dr. Peter Noseworthy, a Mayo Clinic cardiac electrophysiologist who was the senior author on the study, in a press release.     WHY IT MATTERS Ejection fraction is a measurement of how much blood the left ventricle pumps out with each contraction. A lower-than-normal ejection fraction can be a sign of heart failure.   Diagnosing low ejection fraction early can be key to effective treatment. And as the Mayo Clinic notes, an echocardiogram can do so, but it is time-consuming and resource-heavy. By contrast, an EKG is readily available, inexpensive and fast.   The ECG AI-Guided Screening for Low Ejection Fraction, or EAGLE, was aimed at determining whether an AI-enabled EKG algorithm trial could help improve the diagnosis of this condition.   Over eight months, 22,641 adult patients received an EKG under the medical supervision of 348 primary care clinicians throughout Minnesota.    Clinicians in the intervention group were "alerted to a positive screening result for low ejection fraction via the electronic health record, prompting them to order an echocardiogram to confirm," read the press release.   The AI found positive results in 6% of the patients. Although the proportion who received an echocardiogram was similar overall, the AI intervention increased the diagnosis of low ejection fraction.   "To put it in absolute terms, for every 1,000 patients screened, the AI screening yielded five new diagnoses of low ejection fraction over usual care," said Xiaoxi Yao, a health outcomes researcher in cardiovascular diseases at Mayo Clinic and first author on the study, in a statement.   The low ejection fraction algorithm has received Food and Drug Administration breakthrough designation.   "With EAGLE, the information was readily available in the electronic health record, and care teams could see the results and decide how to use that information," said Noseworthy.   THE LARGER TREND Companies have developed several innovations over the past few years aimed at using AI to assist with detecting and diagnosing heart disease.   In 2018, Google AI announced that it had successfully predicted cardiovascular problems using images of the retina – a potential major breakthrough.   A few years later, the FDA approved marketing authorization for AI-enabled cardiac ultrasound software to assist in diagnosis for non-expert providers. And this past month, a study found that a new AI tool may help cardiologists select the best non-invasive diagnostic test.   ON THE RECORD   "The takeaway is that we are likely to see more AI use in the practice of medicine as time goes on. It's up to us to figure how to use this in a way that improves care and health outcomes but does not overburden front-line clinicians," said Noseworthy.   Kat Jercich is senior editor of Healthcare IT News.Twitter: @kjercichEmail: kjercich@himss.orgHealthcare IT News is a HIMSS Media publication.
梅奥诊所本周公布的一项试验结果表明,人工智能有可能帮助一些类型的心脏病的早期诊断。 这项周四发表在《自然医学》上的研究发现,一种AI启用的心电图增加了低射血分数的诊断。 该研究的资深作者、梅奥诊所心脏电生理学家Peter Noseworth博士在一份新闻稿中说:“通过识别以前可能会从裂缝中溜走的人,人工智能辅助的心电图有助于在现实世界中诊断低射血分数的患者。” 为什么重要 射血分数是衡量左心室每次收缩时泵出多少血的指标。射血分数低于正常值可能是心力衰竭的征兆。 早期诊断低射血分数是有效治疗的关键。正如梅奥诊所所指出的,超声心动图可以做到这一点,但它既耗时又耗费资源。相比之下,心电图是现成的,便宜和快速。 ECG AI引导的低射血分数筛查(简称EAGLE),旨在确定AI支持的EKG算法试验是否有助于改善这种情况的诊断。 在明尼苏达州348名初级保健临床医生的医疗监督下,22,641名成年患者接受了心电图检查。 新闻稿中写道,干预组的临床医生“通过电子健康记录得知低射血分数的阳性筛查结果,促使他们命令做超声心动图来确认”。 AI阳性率为6%。虽然接受超声心动图检查的比例总体上是相似的,但AI干预增加了低射血分数的诊断。 梅奥诊所心血管疾病健康结果研究员、该研究的第一作者姚小西在一份声明中说:“从绝对值来看,每筛查1000名患者,AI筛查就会产生5个新的低射血分数诊断。” 低射血分数算法已获得食品和药物管理局的突破性指定。 Noseworth说:“有了EAGLE,电子健康记录中的信息就可以很容易地得到,护理团队可以看到结果,并决定如何使用这些信息。” 更大的趋势 在过去的几年里,一些公司已经开发了几项创新,目的是利用人工智能来帮助检测和诊断心脏病。 2018年,谷歌AI宣布利用视网膜的图像成功预测了心血管问题--这是一个潜在的重大突破。 几年后,FDA批准了支持AI的心脏超声软件的上市授权,以帮助非专家提供商进行诊断。而在过去的这个月,一项研究发现,一种新的AI工具可能会帮助心脏病专家选择最佳的无创诊断测试。 记录在案 Noseworth说:“随着时间的推移,我们很可能会看到更多的人工智能应用于医学实践。这取决于我们如何以一种既能改善护理和健康结果,又不让一线临床医生负担过重的方式使用人工智能。” Kat Jercich是Healthcare IT News的高级编辑。Twitter:@KjercicheMail:Kjercich@HIMSS.orghealthCare IT News是HIMSS的媒体出版物。

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