Clinical study conducted in the Amsterdam OLVG Hospital over a period of 3 years has shown a sensitivity and specificity of 98.1% in addition:
- The shallow neural network showed excellent performance for peak detection.
- High sensitivity and specificity for detection of atrial fibrillation were obtained using a new automated plethysmography (PPG) algorithm.
- Predefined exclusion of recordings with low confidence boosted the diagnostic performance of the algorithm, resulting in 1.8% increase in sensitivity and 4.6% increase in specificity.
Full publication can be read here: https://www.cvdigitalhealthjournal.com/article/S2666-6936(20)30031-1/fulltext
"The algorithm correctly classified AF in 104 patients and incorrectly classified AF in 2 patients. The recording was labeled undetermined in 2 patients. Among the PPG recordings during SR, 101 were correctly classified, 2 were incorrectly classified, and in 5 the algorithm outcome was undetermined."
Performance of an automated photoplethysmography-based artificial intelligence algorithm to detect atrial fibrillation