Video-Based Remote Photoplethysmography for Atrial Fibrillation Detection: A Systematic Review

Atrial fibrillation (AF) is a common cardiac arrhythmia associated with increased risk of stroke and cardiovascular complications. Although electrocardiography (ECG) remains the diagnostic gold standard, remote photoplethysmography (rPPG) has emerged as a promising non-contact approach for extracting pulse-related information from facial videos and enabling unobtrusive rhythm screening. This systematic review investigates the current evidence on facial-video-based rPPG methods for AF detection. Following a PRISMA-based workflow, records were identified from PubMed, Embase, Scopus, IEEE Xplore, and additional search sources. After removing 151 duplicates, 358 records were screened by title and abstract. Of these, 36 full-text reports were assessed for eligibility. Two additional studies, not retrieved by the database search, were identified through reference-list screening, resulting in 19 studies being included in the review. The reviewed literature shows that recent computer-vision and deep learning approaches have improved robustness to motion artifacts, illumination changes, and acquisition noise, often achieving promising detection performance in controlled settings. However, the field remains limited by the small number of available studies, heterogeneous datasets, and scarce clinical validation. Moreover, within the reviewed literature, widespread regulatory-cleared clinical deployment of camera-based rPPG systems for remote AF detection was not identified, indicating that further methodological standardization, larger clinical studies, and regulatory-oriented validation are needed before this technology can become a reliable tool for real-world AF screening.