As the results get extrapolated to the real world, one must be prepared for large numbers of false positives, unnecessary screening and patient anxiety. Also, the test cohort was small and with a very limited number of patients with LV dysfunction to identify. Generalizability is a big concern, especially with regards to the cohort from whom the initial dataset was derived to create the prediction algorithm. It is important to emphasize that this is just an initial step and needs significant refinement before getting anywhere close to clinical applicability. This is a nice study showing the potential of app-based recruitment of patients and the combination of ECG sensors within the Apple Watch with AI for the future risk stratification of patients with LV dysfunction. “This is a great way to keep monitoring patients who are at higher risk - patients with diabetes, patients with hypertension.” Questions remain “The AI low EF model could be for everyone,” Attia said. We found that this was a useful way to scan patients from diverse populations and still apply these tools when the patient is in their own home.”Īttia said adapting ECG monitoring to an AI-enabled smartwatch for use outside the clinic setting could make such screenings scalable for hospital systems to better serve patients, particularly in remote communities. “While uploaded multiple ECGs, we were still able to take even a single ECG, closest to the echo and still get the AUC of 0.88. “This is similar to what you would get from a 12-lead, clinically done ECG,” Attia said. The AUC was 0.875, sensitivity was 81.2% and specificity 81.3%. Among the participants, 3.8% had an EF of 40% or lower, with 13 of the 16 patients identified by the watch AI ECG. Within the cohort, 421 patients had at least one normal sinus rhythm ECG (mean, 17 ECGs) within 30 days before or after an Apple Watch ECG. “Most uploaded about eight times, or once every other week.” “The patients, even though they had no real incentives, they were not paid, kept uploading their ECGs,” Attia said. The researchers assessed area under the receiver operating characteristic curve (AUC), sensitivity and specificity of the AI for EF 40% or lower. Participants uploaded 125,610 ECGs in less than 5 months, with 92% of patients using the app more than once (mean, 7.8 uses per user). Patients downloaded a study app that sent all previously recorded ECGs for clinician review ECGs acquired from the watches within 1 month of a clinically ordered ECG were analyzed by AI for the presence of low EF, using a model adapted for single-lead use. The mean age of participants was 53 years and 56% were women. states and 11 countries, recruited via email, who had a Mayo Clinic app and owned an Apple Watch. The second question was, would this work with an Apple Watch ECG?” Assessing smartwatch dataįor the Mayo ECG Watch study, Attia and colleagues analyzed data from 2,454 patients from 46 U.S. “We were not sure if we could remotely recruit patients and if they would remain engaged. “We asked ourselves, can we use something like the Apple Watch to make this scalable?” Attia said. The AI technology was initially able to detect low EF with an area under the curve of 0.93 and a sensitivity and specificity of 86%, Attia said. “We did that by showing the neural network about 50,000 ECGs of patients who had an echo, and the network was able to learn subtle patterns predicting the presence of low ejection fraction.” “Two years ago, we created a neural network model using a 12-lead ECG to detect what an echo would say,” Attia said during a press conference. Diagnosis of such conditions requires an ECG, MRI or CT, which can be expensive and time-consuming, Attia said, adding an AI model paired with a wearable smart device could prove a useful tool to enable early detection of LV dysfunction. Attia, PhD, MSEE, co-director of AI in cardiology at Mayo Clinic, said during a presentation at Heart Rhythm 2022. HF and LV dysfunction affect about 9% of adults aged 60 years and older, and there are effective treatments to lower mortality and hospitalization risk if diagnosed at an early stage, Zachi I. If you continue to have this issue please contact to HealioĪpplying artificial intelligence to an Apple Watch ECG can reliably and safely identify left ventricular dysfunction in a nonclinical setting, researchers reported in a proof-of-concept study.
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