Identifying patients with atrial fibrillation during sinus rhythm on ECG: Significance of the labeling in the artificial intelligence algorithm

被引：11

作者：

Suzuki, Shinya ^{[1
]}

Motogi, Jun ^{[2
]}

Nakai, Hiroshi ^{[3
]}

Matsuzawa, Wataru ^{[2
]}

Takayanagi, Tsuneo ^{[2
]}

Umemoto, Takuya ^{[2
]}

Hirota, Naomi ^{[1
]}

Hyodo, Akira ^{[2
]}

Satoh, Keiichi ^{[2
]}

Otsuka, Takayuki ^{[1
]}

Arita, Takuto ^{[1
]}

Yagi, Naoharu ^{[1
]}

Yamashita, Takeshi ^{[1
]}

机构：

[1] Cardiovasc Inst, Dept Cardiovasc Med, Tokyo, Japan

[2] Nihon Kohden Corp, Tokyo, Japan

[3] Cardiovasc Inst, Informat Syst Div, Tokyo, Japan

来源：

IJC HEART & VASCULATURE | 2022年 / 38卷

关键词：

Atrial fibrillation; Artificial intelligence; Electrocardiography; ELECTRICAL CARDIOVERSION;

D O I：

10.1016/j.ijcha.2022.100954

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

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.

引用

页数：9