AI allows early diagnosis of low ejection fraction in primary care


An electrocardiogram (EKG)-based artificial intelligence (AI) algorithm based can enable the early diagnosis of low ejection fraction in patients in routine primary care, according to research published in Nature Medicine.

The ECG AI-Guided Screening for Low Ejection Fraction—EAGLE—trial set out to determine whether an AI screening tool developed to detect low ejection fraction using data from an EKG could improve the diagnosis of this condition in routine practice.

Systolic low ejection fraction is defined as the heart’s inability to contract strongly enough with each beat to pump at least 50% of the blood from its chamber. The AI-enabled EKG algorithm was tested and developed through a convolutional neural network and validated in subsequent studies.

The EAGLE trial took place in 45 medical institutions in Minnesota and Wisconsin, USA, including rural clinics, and community and academic medical centres. In all, 348 primary care clinicians from 120 medical care teams were randomly assigned to usual care or intervention. The intervention group was alerted to a positive screening result for low ejection fraction via the electronic health record, prompting them to order an echocardiogram to confirm.

“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,” says Peter Noseworthy, a cardiac electrophysiologist at the Mayo Clinic, Rochester USA, and senior author on the study.

In eight months, 22,641 adult patients had an EKG under the care of the clinicians in the trial. The AI found positive results in 6% of the patients. The proportion of patients who received an echocardiogram was similar overall, but among patients with a positive screening result, a higher percentage of intervention patients received an echocardiogram.

“The AI intervention increased the diagnosis of low ejection fraction overall by 32% relative to usual care. Among patients with a positive AI result, the relative increase of diagnosis was 43%,” says Xiaoxi, a health outcomes researcher in cardiovascular diseases at Mayo Clinic and first author on the study. “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.”

“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,” says Noseworthy. “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.”


  1. Nice example of real world evidence in the application of deep learning convoluted neural networks that have been developed by unsupervised learning. This demonstrates how AI can be seamlessly integrated into clinical workflow to make healthcare more efficient and most importantly potentially improve the care of the patient.


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