South Korean researchers are working on packing the ability to monitor heart health and detect signs of atrial fibrillation into what might be the smallest cardiology wearable to date: a “smart ring.”
The proposed device, a more compact alternative to its predecessors, is backed by a deep learning algorithm, VentureBeat reported May 9. Research presented at this week’s Heart Rhythm Society meeting in San Francisco used ECGs and optical sensor-based photoplethysmographs from 119 AFib patients to train a convolutional neural network to diagnose AFib and sinus rhythm.
Ultimately, Eue-Keun Choi, MD, PhD, and colleagues achieved 100% accuracy in diagnosing AFib and 98.3% accuracy in diagnosing a regular sinus rhythm with their model.
“The diagnostic performance is comparable to medical-grade conventional pulse oximeters,” Choi said. “We would like to evaluate the deep learning algorithm with a newly developed ring device in daily activity. This will provide feasibility for AFib screening in a high-risk population.”
He also said he hoped the ring device could be used in future clinical trials to detect AFib, since it’s so noninvasive.
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