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| Trials show that AI guidance can help early detection of heart disease in routine practice |
Certain types of heart disease may be difficult to detect, such as asymptomatic low ejection fraction, especially in the early stages when treatment is most effective.
A low systolic ejection fraction is defined as the inability of the heart to contract strongly enough at each beat to draw at least 50% of the blood from its chambers.
The EAGLE trial was conducted in 45 medical institutions in Minnesota and Wisconsin, including rural clinics, community, and academic medical centers.
"AI-supported ECG can identify patients who may have been missed before, and help diagnose patients with low ejection fraction in reality.
Within 8 months, 22,641 adult patients underwent ECG under the care of the clinician in the trial.
“Compared with conventional care, AI intervention has increased the overall diagnosis rate of low ejection fraction by 32%.
"With EAGLE, this information can be obtained in the electronic medical record at any time, and the nursing team can see the results and decide how to use the information," Dr.
In addition, the EAGLE trial used a positive bias method to evaluate the top five nursing team users and the top five non-users of AI screening information.
EAGLE is one of the first large-scale experiments to prove the value of AI in routine practice.
The EAGLE trial was funded by the Robert D.
)
Related paper information: org/10.
org/10.
1038/s41591-021-01335-4" target="_blank">https://doi.
org/10.
1038/s41591-021-01335-4
