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

Suzuki, Shinya; Motogi, Jun; Nakai, Hiroshi; Matsuzawa, Wataru; Takayanagi, T; Umemoto, Takuya; Hirota, Naomi; Hyodo, Akira; Satoh, Keiichi; Otsuka, Takayuki; Arita, Takuto; Yagi, Nobuyuki; Yamashita, Takeshi

doi:10.1016/j.ijcha.2022.100954

Cited by 6 publications

(11 citation statements)

References 18 publications

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“…A central point of our investigation is the relationship between AI-enhanced ECG findings and the maintenance of SR in patients undergoing AF-related procedures. A recent study also highlighted the impressive performance of AI-enabled ECG in detecting AF on SR-ECG, showing increased efficacy when the algorithm incorporated SR-ECG after the index AF-ECG ( 20 ). This is consistent with the “window period” approach in our previous study.…”

Section: Discussionmentioning

confidence: 99%

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

Baek,

Kwon,

You

et al. 2023

Front. Cardiovasc. Med.

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IntroductionAtrial 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 analysisWe 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.DiscussionThis 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 disseminationThe 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[cris.nih.go.kr], identifier (KCT0007881).

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Section: Discussionmentioning

confidence: 99%

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

Baek,

Kwon,

You

et al. 2023

Front. Cardiovasc. Med.

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show abstract

“…The CNN model in the present study was constructed based on the model by Attia et al [14] , [15] , [18] The detail architecture of the CNN model is shown in supplementary Fig. 2 .…”

Section: Methodsmentioning

confidence: 99%

“…Recently, we have created a digital ECG dataset connected to our single-center cohort database and have reported several conventional machine learning models [11] , [12] , [13] as well as CNN models [14] , [15] for predicting heart age, atrial fibrillation, cardiovascular events, and mortality. Leveraging this database, our objective was to develop a CNN model using ECGs for the detection of AD, with the goal of enhancing the AD screening strategy.…”

Section: Introductionmentioning

confidence: 99%

Identifying patients with acute aortic dissection using an electrocardiogram with convolutional neural network

Arita,

Suzuki,

Motogi

et al. 2024

IJC Heart & Vasculature

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“…We constructed a CNN using the Keras Framework with a Tensorflow (Google; Mountain View, CA, USA) backend and Python (Python Software Foundation, Beaverton, OR, USA). The architecture of the CNN in the present study was inspired by previous studies [21] , [22] . The CNN model had layers for a temporal axis and a lead axis [21] .…”

Section: Methodsmentioning

confidence: 99%

Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms

Hirota¹,

Suzuki²,

Motogi³

et al. 2023

IJC Heart & Vasculature

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

Cited by 6 publications

References 18 publications

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

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

Identifying patients with acute aortic dissection using an electrocardiogram with convolutional neural network

Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms

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