Key result
PPG smartband with AI detects atrial fibrillation with up to ~98% sensitivity versus ECG.
Why the study?
Early detection of AF is challenging yet crucial to prevent complications, and the performance of three PPG-based AI algorithms on a medical smartband needed evaluation against gold-standard ECG.
Does a medical smartband with PPG technology and artificial intelligence algorithms accurately detect atrial fibrillation in adult patients compared to standard ECG?
Observational (n=72)
Yes
Does a medical smartband with PPG technology and artificial intelligence algorithms accurately detect atrial fibrillation in adult patients compared to standard ECG?
Wearable medical smartbands utilizing PPG sensors and AI algorithms demonstrate high sensitivity and specificity for detecting atrial fibrillation in analyzable recordings, showing promise for remote arrhythmia monitoring.
May aid remote AF monitoring; hypothesis-generating and requires larger prospective validation before practice change.
Background: Atrial fibrillation (AF) is a prevalent arrhythmia with significant public health implications, including increased risk of stroke and mortality. Early detection is challenging but crucial for managing complications. Wearable technology with photoplethysmography (PPG) offers a potential solution for long-term, non-invasive monitoring. This study aims to evaluate the performance of three artificial intelligence (AI) algorithms (Happitech, Preventicus, and Philips Biosensing AF) in detecting AF using PPG signals from a medical smartband and compare it with the gold standard electrocardiogram (ECG). Methods: A medical smartband equipped with PPG technology was used to collect cardiovascular data from patients with and without AF. The sensitivity and specificity of the algorithm for detecting AF were determined by comparing their output to a trained technician's examination of concurrent ECG recordings. Results: Seventy two participants (42% female, 57±17 years old) were included in this study. The medical smartband provided continuous PPG signals, with AI algorithms evaluating the data for AF episodes. The accuracy of AF detection by the algorithms was compared with that of the concurrent ECG recordings. Sensitivity varied between 80.0% (62.5-97.5%) and 97.6% (97.6-97.6%), specificity between 90.6% (80.5-100%) and 96.9% (90.8-100%). Conclusions: This study demonstrates the potential of medical smartbands combined with PPG technology and AI algorithms for reliable AF detection. The findings suggest a promising direction for remote AF monitoring and early intervention, potentially reducing AF-related complications and healthcare costs.
No takes yet. Share an insight, caveat, or question.
Blok et al. (2025) conducted an observational in Atrial Fibrillation (n=72). Medical smartband with photoplethysmography (PPG) and AI algorithms (Happitech, Preventicus, Philips Biosensing) vs. Concurrent electrocardiogram (12-lead ECG or Holter) was evaluated on Diagnostic accuracy (sensitivity and specificity) for detecting atrial fibrillation compared to ECG. A medical smartband using photoplethysmography and AI algorithms detected atrial fibrillation with sensitivity ranging from 80.0% to 97.6% and specificity from 90.6% to 96.9% compared to ECG.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: