Artificial intelligence-enhanced 12-lead electrocardiography for identifying atrial fibrillation during sinus rhythm (AIAFib) trial: protocol for a multicenter retrospective study

Yong Soo Baek, Soonil Kwon, Seng Chan You, Kwang No Lee, Hee Tae Yu, So Ryung Lee, Seung Young Roh, Dong Hyeok Kim, Seung Yong Shin, Dae In Lee, Junbeom Park, Yae Min Park, Young Ju Suh, Eue Keun Choi, Sang Chul Lee, Boyoung Joung, Wonik Choi, Dae Hyeok Kim

Research output: Contribution to journalArticlepeer-review


Introduction: Atrial fibrillation (AF) is the most common arrhythmia, contributing significantly to morbidity and mortality. In a previous study, we developed a deep neural network for predicting paroxysmal atrial fibrillation (PAF) during sinus rhythm (SR) using digital data from standard 12-lead electrocardiography (ECG). The primary aim of this study is to validate an existing artificial intelligence (AI)-enhanced ECG algorithm for predicting PAF in a multicenter tertiary hospital. The secondary objective is to investigate whether the AI-enhanced ECG is associated with AF-related clinical outcomes. Methods and analysis: We will conduct a retrospective cohort study of more than 50,000 12-lead ECGs from November 1, 2012, to December 31, 2021, at 10 Korean University Hospitals. Data will be collected from patient records, including baseline demographics, comorbidities, laboratory findings, echocardiographic findings, hospitalizations, and related procedural outcomes, such as AF ablation and mortality. De-identification of ECG data through data encryption and anonymization will be conducted and the data will be analyzed using the AI algorithm previously developed for AF prediction. An area under the receiver operating characteristic curve will be created to test and validate the datasets and assess the AI-enabled ECGs acquired during the sinus rhythm to determine whether AF is present. Kaplan–Meier survival functions will be used to estimate the time to hospitalization, AF-related procedure outcomes, and mortality, with log-rank tests to compare patients with low and high risk of AF by AI. Multivariate Cox proportional hazards regression will estimate the effect of AI-enhanced ECG multimorbidity on clinical outcomes after stratifying patients by AF probability by AI. Discussion: This study will advance PAF prediction based on AI-enhanced ECGs. This approach is a novel method for risk stratification and emphasizes shared decision-making for early detection and management of patients with newly diagnosed AF. The results may revolutionize PAF management and unveil the wider potential of AI in predicting and managing cardiovascular diseases. Ethics and dissemination: The study findings will be published in peer-reviewed publications and disseminated at national and international conferences and through social media. This study was approved by the institutional review boards of all participating university hospitals. Data extraction, storage, and management were approved by the data review committees of all institutions. Clinical Trial Registration: [], identifier (KCT0007881).

Original languageEnglish
Article number1258167
JournalFrontiers in Cardiovascular Medicine
StatePublished - 2023

Bibliographical note

Publisher Copyright:
2023 Baek, Kwon, You, Lee, Yu, Lee, Roh, Kim, Shin, Lee, Park, Park, Suh, Choi, Lee, Joung, Choi and Kim.


  • artificial intelligence
  • atrial fibrillation
  • deep learning
  • electrocardiography
  • neural networks


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