Key result
Continuous monitoring using a photoplethysmography-based convolutional neural network on a wrist-worn device detected atrial fibrillation with 96.1% sensitivity and 98.1% specificity.
Why the study?
Existing consumer wrist-worn devices perform only periodic checks when stationary and lack clearance for clinical decision-making, leaving an unmet need for medical-grade devices providing continuous AF monitoring.
Does a wrist-worn device with continuous photoplethysmography and a deep learning algorithm accurately detect atrial fibrillation and estimate AF burden compared to a 14-day continuous ECG monitor in patients with paroxysmal AF?
Population
117 patients with paroxysmal AF
Comparison
Wrist-worn continuous PPG device vs 14-day continuous ECG monitor
Design
Prospective multicenter study
Follow-up
14-day continuous monitoring
Authors
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High sensitivity/specificity supports continuous ambulatory AF monitoring with wrist-worn PPG devices; leaves open need for outcome trials before clinical adoption.
Observational (n=117)
Yes
Does a wrist-worn device with continuous photoplethysmography and a deep learning algorithm accurately detect atrial fibrillation and estimate AF burden compared to a 14-day continuous ECG monitor in patients with paroxysmal AF?
Effect estimate: 96.1% sensitivity and 98.1% specificity (95% CI 92.7-98.0 for sensitivity, 97.2-99.1 for specificity)
A wrist-worn device using continuous photoplethysmography and a deep learning algorithm demonstrated high sensitivity and specificity for detecting atrial fibrillation and accurately estimated AF burden compared to a 14-day continuous ECG monitor.
Poh et al. (2023) conducted an observational in Paroxysmal atrial fibrillation (n=117). Verily Study Watch with photoplethysmography-based convolutional neural network vs. 14-day continuous ECG monitor (Zio XT) was evaluated on Interval-level atrial fibrillation detection (96.1% sensitivity and 98.1% specificity, 95% CI 92.7-98.0 for sensitivity, 97.2-99.1 for specificity). Continuous monitoring using a photoplethysmography-based convolutional neural network on a wrist-worn device detected atrial fibrillation with 96.1% sensitivity and 98.1% specificity.
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