Health
AI Detection of Cardiac Dysfunction from Consumer Watch ECG Recordings
In a world where AI models have superhuman abilities3 Because health sensors are ubiquitous, heart disease may be identified earlier through home monitoring using a more continuous and less intrusive method than current screening approaches. This will help identify those who can benefit from established treatments and those who do not need it, making better use of overburdened medical resources. Along with this opportunity is the obligation to rigorously test AI in real-world scenarios and demonstrate its effectiveness before using it at scale.FourUsing digital remote studies, we have shown that an AI-ECG model can be adapted to effectively classify real Apple Watch data. In addition to validation of specific models, the use of mobile phone apps remains an affordable, fast and reliable method of data collection and continues to be of high interest to patients of all ages (ages 18 to 90). It turns out that I have.
A potential limitation of our study is the cost of the Apple Watch. Although these devices have become more affordable and their use is increasing, their application in healthcare may exacerbate healthcare inequities. However, distributing watches to clinics in underserved settings for use as a shared resource may allow for very cost-effective screening. A second limitation is the limited racial diversity of the study population. Although the original 12-lead AI-ECG model has been found to be robust across races and ethnicities,Fivewhich should also be evaluated with a watch ECG.
We plan to expand collection of ECGs from mobile devices made by any vendor that allows access to the data (Apple Watch was used for open access to raw ECGs available via HealthKit). .Apple was unaware and did not provide support until the study was completed ) ) They also allowed patients to upload their data to the AI-ECG dashboard and actually detected by the AI-ECG. It enables prospective screening for ventricular dysfunction and other conditions that have been shown to be possible, and facilitates access to care for local and regional patients. Areas with limited resources.
Zachi I. Attia and Paul A. Friedman, Mayo Clinic, Rochester, Minnesota, USA.
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