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Published on: February 26, 2013
Evaluating AI-Enabled Technologies for Atrial Fibrillation Detection: A Systematic Review of Diagnostic Performance
Maddison Weber1, Mason Klisares, May Li-Jedras
1Creighton University Medical Center; 7500 Mercy Road, Omaha, NE, 68124, USA.
Insights
Artificial intelligence (AI) and wearable devices show potential for detecting atrial fibrillation (AF). Performance varies, especially with consumer devices, necessitating further validation for widespread clinical use.
Area of Science:
- Cardiology
- Digital Health
- Artificial Intelligence
Background:
- Atrial fibrillation (AF) is a common arrhythmia linked to stroke and cardiovascular issues.
- Current AF detection methods have limitations in accessibility and monitoring duration.
- AI and wearable technologies offer new avenues for early AF detection.
Purpose of the Study:
- To systematically review the diagnostic performance of AI-enabled and wearable technologies for AF detection.
- To evaluate these technologies in both clinical and real-world settings.
Main Methods:
- Comprehensive literature search of major databases (PubMed, Scopus, Web of Science).
- Inclusion of studies reporting sensitivity, specificity, predictive values, and AUC for digital AF detection tools.
- Data extraction and quality assessment using QUADAS-2, followed by random-effects meta-analyses.
Main Results:
- Twenty-four studies were included, with high-performing tools achieving sensitivity and specificity ≥94%.
- Consumer-grade devices showed lower specificity (46%) and positive predictive value (7.6%), indicating frequent false positives.
- Heterogeneity was observed, influenced by device type, signal source (PPG vs. ECG), algorithm, and population characteristics.
Conclusions:
- AI-enhanced and wearable technologies demonstrate significant potential for AF detection under specific conditions.
- Performance variability, particularly in consumer devices, highlights the need for external validation and algorithm refinement.
- Future research should focus on real-world effectiveness, cost-efficiency, and explainability for broader clinical adoption.
Background:
Atrial fibrillation (AF) is the most common sustained arrhythmia worldwide and a major contributor to stroke and cardiovascular morbidity. Conventional diagnostics, including electrocardiograms (ECG) and ambulatory monitors, are limited by accessibility and short monitoring windows, particularly in asymptomatic or paroxysmal AF. Recently, artificial intelligence (AI)-driven technologies and wearable devices have emerged as promising tools for early AF detection.
Objective:
This systematic review evaluates the diagnostic performance of AI-enabled and wearable technologies for AF detection in clinical and real-world settings.
Methods:
A comprehensive search of PubMed, Scopus, and Web of Science identified studies reporting sensitivity, specificity, predictive values, and AUC of digital AF detection tools. Platforms included smartphone apps, smartwatches, photoplethysmography (PPG), single- and multi-lead ECGs, and machine learning algorithms. Data extraction and quality assessment used QUADAS-2. Random-effects meta-analyses and subgroup analyses synthesized findings and explored heterogeneity.
Results:
Twenty-four studies met inclusion criteria. High-performing tools demonstrated sensitivity and specificity ≥94% and AUC ≥0.95, while consumer-grade devices, such as the Apple Heart Study, showed lower specificity (46%) and positive predictive value (7.6%), reflecting frequent false positives. Heterogeneity arose from device type, signal method (PPG vs. ECG), algorithm design, and population characteristics. Tools integrating explainable AI and multi-modal data generally outperformed simpler models.
Conclusions:
AI-enhanced and wearable technologies show strong potential for accurate AF detection under specific conditions. Performance variability, especially in consumer-grade devices, underscores the need for external validation, algorithm refinement, and clinical integration. Future research should assess real-world effectiveness, cost-efficiency, explainability, and long-term outcomes to support broader adoption.
