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.
Read the full story from VentureBeat below:
After graduating from Indiana University-Bloomington with a bachelor’s in journalism, Anicka joined TriMed’s Chicago team in 2017 covering cardiology. Close to her heart is long-form journalism, Pilot G-2 pens, dark chocolate and her dog Harper Lee.