Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis

Abstract

Atrial fibrillation (AF) is the most common arrhythmia with a high burden of morbidity including impaired quality of life and increased risk of thromboembolism. Early detection and management of AF could prevent thromboembolic events. Artificial intelligence (AI)--based methods in healthcare are developing quickly and can be proved as valuable for the detection of atrial fibrillation. In this metanalysis, we aim to review the diagnostic accuracy of AI-based methods for the diagnosis of atrial fibrillation. A predetermined search strategy was applied on four databases, the PubMed on 31 August 2022, the Google Scholar and Cochrane Library on 3 September 2022, and the Embase on 15 October 2022. The identified studies were screened by two independent investigators. Studies assessing the diagnostic accuracy of AI-based devices for the detection of AF in adults against a gold standard were selected. Qualitative and quantitative synthesis to calculate the pooled sensitivity and specificity was performed, and the QUADAS-2 tool was used for the risk of bias and applicability assessment. We screened 14,770 studies, from which 31 were eligible and included. All were diagnostic accuracy studies with case–control or cohort design. The main technologies used were: (a) photoplethysmography (PPG) with pooled sensitivity 95.1% and specificity 96.2%, and (b) single-lead ECG with pooled sensitivity 92.3% and specificity 96.2%. In the PPG group, 0% to 43.2% of the tracings could not be classified using the AI algorithm as AF or not, and in the single-lead ECG group, this figure fluctuated between 0% and 38%. Our analysis showed that AI-based methods for the diagnosis of atrial fibrillation have high sensitivity and specificity for the detection of AF. Further studies should examine whether utilization of these methods could improve clinical outcomes.

Ambulatory Atrial Fibrillation Detection and Quantification by a wrist-worn AI-device Compared to Standard Holter Monitoring

“…The exclusion of unanalysable data may have affected the outcomes of this study. Nonetheless, the amount of unanalysable PPG data and single-lead ECG attempts was in line with the results presented by other studies [23]. Even though a large amount of analysable 90-second PPG windows were available, AF episodes were relatively small due to the limited number of patients with AF.…”

“…The median sensitivity and specificity achieved in an ambulatory setting were often lower for both PPG-and single-lead ECG-based evaluations [23][24][25]. The sensitivity and specificity obtained by the CW2's PPGand single-lead ECG-based algorithm are equal to those achieved within clinical studies.…”

“…As the CW2 can be used over an extended period, patient compliance, the number of ECG attempts before a successful ECG, and the AF confirmation rate according to the single-lead ECG algorithm are essential to the eventual clinical implementation. However, these results were difficult to compare to the literature, as the measurement period used here is one of the longest considered [10,23]. This extended measurement period proved especially useful in cases of paroxysmal AF, as this led to an increase in AF detection and prevalence compared to standard Holter recordings.…”

“…Various studies have previously investigated the performance of PPG and single-lead ECG algorithms for AF detection. When considering PPG-based AF detection algorithms alone, the median sensitivity and specificity, according to several (systematic) reviews, ranged from 95.1% to 100.0% and 95.0% to 97.7%, respectively [9,10,23]. For single-lead ECG-based AF detection algorithms, the sensitivity and specificity lie between 75.1% to 98.0% and 84.0% to 98.2%, respectively [23][24][25].…”

“…When considering PPG-based AF detection algorithms alone, the median sensitivity and specificity, according to several (systematic) reviews, ranged from 95.1% to 100.0% and 95.0% to 97.7%, respectively [9,10,23]. For single-lead ECG-based AF detection algorithms, the sensitivity and specificity lie between 75.1% to 98.0% and 84.0% to 98.2%, respectively [23][24][25]. However, many of these studies were conducted in a controlled clinical setting and not in an ambulatory environment.…”