Identifying patients with atrial fibrillation during sinus rhythm on ECG: Significance of the labeling in the artificial intelligence algorithm. (February 2022)

• Record Type: Journal Article
• Authors: Suzuki, Shinya; Motogi, Jun; Nakai, Hiroshi; Matsuzawa, Wataru; Takayanagi, Tsuneo; Umemoto, Takuya; Hirota, Naomi; Hyodo, Akira; Satoh, Keiichi; Otsuka, Takayuki; Arita, Takuto; Yagi, Naoharu; Yamashita, Takeshi
• Is Part Of: IJC heart & vasculature. Volume 38(2022)
• Publication Date: 2022-02
• Subjects: Atrial fibrillation -- Artificial intelligence -- Electrocardiography; Cardiovascular system -- Diseases -- Periodicals; Cardiovascular system -- Pathophysiology -- Periodicals; 616.1005
• DOI: 10.1016/j.ijcha.2022.100954
• Languages: English
• ISSNs: 2352-9067

Abstract

Highlights: High performance of AI algorithm to detect AF using SR-ECG was confirmed in patients without structural heart disease. The performance of AI-enabled ECG to detect AF was high especially when the algorithm included SR-ECG taken after the index AF-ECG. A similar tendency was observed when the performance was tested in patients with structural heart diseases. Abstract: Background: This study aimed to increase the knowledge on how to enhance the performance of artificial intelligence (AI)-enabled electrocardiography (ECG) to detect atrial fibrillation (AF) on sinus rhythm ECG (SR-ECG). Methods: It is a retrospective analysis of a single-center, prospective cohort study (Shinken Database). We developed AI-enabled ECG using SR-ECG to predict AF with a convolutional neural network (CNN). Among new patients in our hospital (n = 19, 170), 276 AF label (having ECG on AF [AF-ECG] in the ECG database) and 1896 SR label with following three conditions were identified in the derivation dataset: (1) without structural heart disease, (2) in AF label, SR-ECG was taken within 31 days from AF-ECG, and (3) in SR label, follow-up ≥ 1, 095 days. Three patterns of AF label were analyzed by timing of SR-ECG to AF-ECG (before/after/before-or-after, CNN algorithm 1 to 3). The outcome measurement was area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score. As an extra-testing dataset, the performance of AI-enabled ECG was tested in patients with structural heart disease. Results: The AUC of AI-enabled ECG with CNN algorithm 1, 2, and 3 in the derivation dataset was 0.83, 0.88, and 0.86, respectively; when tested in patients with structural heart disease, 0.75, 0.81, and 0.78, respectively. Conclusion: We confirmed high performance of AI-enabled ECG to detect AF on SR-ECG in patients without structural heart disease. The performance enhanced especially when SR-ECG after index AF-ECG was included in the algorithm, which was consistent in patients with structural heart disease.