Diagnostic accuracy of an artificial intelligence-enhanced pulse oximeter for atrial fibrillation detection: a real-world population study.

PURPOSE Fingertip-based photoplethysmographic (PPG) devices offer a noninvasive method for at-home atrial fibrillation (AF) screening. In this study, we evaluated the accuracy of a fingertip pulse oximeter enhanced with a supervised artificial intelligence (AI) algorithm in diagnosing AF in a real-world setting.

MATERIALS AND METHODS This prospective, multicenter study included 401 individuals who underwent concurrent assessment using an AI-enhanced fingertip pulse oximeter and standard 12-lead electrocardiography (ECG), with expert-interpreted ECG serving as the diagnostic reference standard. The oximeter measured pulse rate, oxygen saturation, perfusion index (PI; a PPG-derived indicator of peripheral perfusion), and PPG waveforms, which were analyzed with an embedded AI module to classify heart rhythm as "normal," "irregular," or "possible AF."

RESULTS When benchmarked against expert-interpreted ECG, the AI-enhanced pulse oximeter demonstrated high diagnostic performance for AF detection, with a sensitivity of 96.7%, specificity of 90.5%, positive predictive value of 91.9%, and negative predictive value of 96.1%. AF detection rates derived from the AI-enhanced pulse oximeter were consistent with those identified by computer-read 12-lead ECG (55.4% vs. 52.6%, p = 0.436). Diagnostic performance remained high across all clinical subgroups, with a minor decrease observed among individuals with a perfusion index value of ≤1%. No severe device-related adverse events were reported.

CONCLUSIONS The portable, site-less design of this fingertip-based PPG device incorporating an AI algorithm supports its scalability for remote, real-time rhythm monitoring and population-level AF screening.