Cardiology and CTVS Medicine

AI Accurately Detects Cardiac Dysfunction Using Smartwatch ECG: Study

• byDoctor News Daily Team
• 18 February, 2025
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• 0 Mins

USA: Based on their findings published in Nature Medicine, a team of researchers from the Mayo Clinic has opened up the possibility of using measurements obtained at home to find and treat patients (with cardiac dysfunction) before progressing to more-severe disease.
The proof-of-concept study found that an artificial intelligence (AI) algorithm trained to interpret single-lead electrocardiograms (ECGs) from Apple Watch helped accurately detect signs of subclinical left ventricular systolic dysfunction. Cardiac dysfunction is a potentially life-threatening and often asymptomatic condition.

AI algorithms have been demonstrated to have the potential to identify cardiac dysfunction, defined as ejection fraction (EF) ≤ 40%, from 12-lead ECGs; however, cardiac dysfunction identification using single-lead ECG of a smartwatch requires cardiac dysfunction identification using single-lead ECG of a smartwatch testing. Paul A. Friedman, Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA, and colleagues, therefore, conducted a prospective study in which Mayo Clinics' patients were invited by email to download an iPhone application of Mayo Clinic that sends watch ECGs to a secure data platform. They also examined patient engagement with the study app and diagnostic use of the ECGs.
For this purpose, the researchers digitally enrolled 2,454 unique patients from 46 states in the US and 11 countries who sent 125,610 ECGs to the data platform from August 2021 to February 2022.
The study led to the following findings:
Four hundred twenty-one participants had at least one watch-classified sinus rhythm ECG within 30 d of an echocardiogram, of whom 3.8% had an EF ≤ 40%.
The AI algorithm detected patients with low ejection fraction with an area under the curve (AUC) of 0.885 and 0.881, using the mean prediction within a 30-d window or the closest ECG relative to the echocardiogram that determined the EF, respectively.
"The results suggest that consumer watch ECGs, acquired in nonclinical environments, can be used for the identification of patients with cardiac dysfunction, which is a potentially life-threatening and often asymptomatic condition," the authors wrote in their study.
According to the experts, "the approach could find a place in clinical practice if the algorithm is validated in future research and implementation issues are worked out. However, the approach should not replace echocardiography or another way of proper measurement of LV ejection fraction but is potentially useful as a screening tool."
The researcher stated, "we have been advised that, without the approval of the US FDA, the AI algorithm cannot be used in routine practice outside the Mayo Clinic."
Reference:
Attia, Z.I., Harmon, D.M., Dugan, J. et al. Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction. Nat Med (2022). https://doi.org/10.1038/s41591-022-02053-1