ArticleEuropace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology2025
External validation of a machine learning-based classification algorithm for ambulatory heart rhythm diagnostics in pericardioversion atrial fibrillation patients using smartphone photoplethysmography: the SMARTBEATS-ALGO study.
Article in Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04300270 (Validation of a Novel Smartphone-based Photoplethysmographic Method for Ambulatory Heart Rhythm Monitoring in Connection to Treatment of Atrial Fibrillation With Direct Current Cardioversion), which is not on this map. Cited by 5 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Validation of a Novel Smartphone-based Photoplethysmographic Method for Ambulatory Heart Rhythm Monitoring in Connection to Treatment of Atrial Fibrillation With Direct Current Cardioversion
Who cites it
5 citing papers in PubMed.
- A paradigm shift toward full-cycle management of atrial fibrillation: integrating digital twins and artificial intelligence.Frontiers in cardiovascular medicine · 2026Review
- Application of Artificial Intelligence in the Early Detection and Management of Atrial Fibrillation: State-of-the-art Review.European cardiology · 2026Review
- Monitoring Atrial Fibrillation Using Wearable Digital Technologies: The Emerging Role of Smartwatches.Journal of clinical medicine · 2025Review
- Ex Vivo Traceability Platform for Phospholipoproteomic Formulations: Functional Evidence Without Clinical Exposure.Biomedicines · 2025Article
- Review
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Authors and funding
6 authors.
Funding
Abstract
aimsThe aim of this study was to perform an external validation of an automatic machine learning (ML) algorithm for heart rhythm diagnostics using smartphone photoplethysmography (PPG) recorded by patients with atrial fibrillation (AF) and atrial flutter (AFL) pericardioversion in an unsupervised ambulatory setting. METHODS AND
resultsPatients undergoing cardioversion for AF or AFL performed 1-min heart rhythm recordings pericardioversion at least twice daily for 4-6 weeks, using an iPhone 7 smartphone running a PPG application (CORAI Heart Monitor) simultaneously with a single-lead electrocardiogram (ECG) recording (KardiaMobile). The algorithm uses support vector machines to classify heart rhythm from smartphone-PPG. The algorithm was trained on PPG recordings made by patients in a separate cardioversion cohort. Photoplethysmography recordings in the external validation cohort were analysed by the algorithm. Diagnostic performance was calculated by comparing the heart rhythm classification output to the diagnosis from the simultaneous ECG recordings (gold standard). In total, 460 patients performed 34 097 simultaneous PPG and ECG recordings, divided into 180 patients with 16 092 recordings in the training cohort and 280 patients with 18 005 recordings in the external validation cohort. Algorithmic classification of the PPG recordings in the external validation cohort diagnosed AF with sensitivity, specificity, and accuracy of 99.7%, 99.7% and 99.7%, respectively, and AF/AFL with sensitivity, specificity, and accuracy of 99.3%, 99.1% and 99.2%, respectively.
conclusionA machine learning-based algorithm demonstrated excellent performance in diagnosing atrial fibrillation and atrial flutter from smartphone-PPG recordings in an unsupervised ambulatory setting, minimizing the need for manual review and ECG verification, in elderly cardioversion populations. CLINICAL
trial registrationClinicaltrials.gov, NCT04300270.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.