Jasper Hennecken, Bauke K. O. Arends, Thomas Mast, Lukas Dekker, Pim van der Harst, Yuri Blaauw, Wolfgang Dichtl, Thomas Senoner, Rutger J. Hassink, Peter Loh, René van Es, Rutger R. van de Leur
{"title":"基于脑电图的多任务深度学习在Wolff-Parkinson-white综合征中的辅助通路定位","authors":"Jasper Hennecken, Bauke K. O. Arends, Thomas Mast, Lukas Dekker, Pim van der Harst, Yuri Blaauw, Wolfgang Dichtl, Thomas Senoner, Rutger J. Hassink, Peter Loh, René van Es, Rutger R. van de Leur","doi":"10.1111/eci.14385","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Wolff-Parkinson-White syndrome is characterized by accessory atrioventricular pathways (AP) and atrio-ventricular re-entry arrhythmias. Catheter ablation approach and success are determined by AP location. Existing rule-based algorithms based on the electrocardiogram (ECG) are time consuming, prone to inter-observer variability and use delta wave polarity as a binary variable. To overcome these challenges, we propose a model based on a deep neural network (DNN).</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Patients with concealed pathways, multiple antegrade conducting pathways or without any sinus rhythm ECGs were excluded. AP location was determined based on electrophysiological testing during catheter ablation and categorized into right-sided, septal and left-sided APs. Multi-task learning with auxiliary identification of the presence of pre-excitation, parahisian pathways and locations where a transseptal puncture is potentially required was used to increase usability and performance. The DNN was compared to the Milstein and Arruda algorithms.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Between 1997 and 2023, 645 patients who underwent catheter ablation for an AP were included in the study. The model was developed using 1.394 ECGs from 567 patients. The DNN was tested using 78 ECGs in two independent cohorts. The model outperformed both the Milstein and Arruda algorithms with an area under the receiver operating characteristic curve (AUROC) of .92 (95% confidence interval: .88–.96) compared to the Arruda algorithm (AUROC of .80; <i>p</i> <.001) and the Milstein algorithm (AUROC of .81; <i>p</i> <.001).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Our model showed excellent discriminatory performance in predicting the location of an accessory pathway while outperforming conventional techniques. Clinically, this tool can improve preoperative planning and risk stratification.</p>\n </section>\n </div>","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":"55 S1","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/eci.14385","citationCount":"0","resultStr":"{\"title\":\"Localization of accessory pathways in Wolff-Parkinson-white syndrome using ECG-based multi-task deep learning\",\"authors\":\"Jasper Hennecken, Bauke K. O. Arends, Thomas Mast, Lukas Dekker, Pim van der Harst, Yuri Blaauw, Wolfgang Dichtl, Thomas Senoner, Rutger J. Hassink, Peter Loh, René van Es, Rutger R. van de Leur\",\"doi\":\"10.1111/eci.14385\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Wolff-Parkinson-White syndrome is characterized by accessory atrioventricular pathways (AP) and atrio-ventricular re-entry arrhythmias. Catheter ablation approach and success are determined by AP location. Existing rule-based algorithms based on the electrocardiogram (ECG) are time consuming, prone to inter-observer variability and use delta wave polarity as a binary variable. To overcome these challenges, we propose a model based on a deep neural network (DNN).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Patients with concealed pathways, multiple antegrade conducting pathways or without any sinus rhythm ECGs were excluded. AP location was determined based on electrophysiological testing during catheter ablation and categorized into right-sided, septal and left-sided APs. Multi-task learning with auxiliary identification of the presence of pre-excitation, parahisian pathways and locations where a transseptal puncture is potentially required was used to increase usability and performance. The DNN was compared to the Milstein and Arruda algorithms.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Between 1997 and 2023, 645 patients who underwent catheter ablation for an AP were included in the study. The model was developed using 1.394 ECGs from 567 patients. The DNN was tested using 78 ECGs in two independent cohorts. The model outperformed both the Milstein and Arruda algorithms with an area under the receiver operating characteristic curve (AUROC) of .92 (95% confidence interval: .88–.96) compared to the Arruda algorithm (AUROC of .80; <i>p</i> <.001) and the Milstein algorithm (AUROC of .81; <i>p</i> <.001).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Our model showed excellent discriminatory performance in predicting the location of an accessory pathway while outperforming conventional techniques. 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Localization of accessory pathways in Wolff-Parkinson-white syndrome using ECG-based multi-task deep learning
Background
Wolff-Parkinson-White syndrome is characterized by accessory atrioventricular pathways (AP) and atrio-ventricular re-entry arrhythmias. Catheter ablation approach and success are determined by AP location. Existing rule-based algorithms based on the electrocardiogram (ECG) are time consuming, prone to inter-observer variability and use delta wave polarity as a binary variable. To overcome these challenges, we propose a model based on a deep neural network (DNN).
Methods
Patients with concealed pathways, multiple antegrade conducting pathways or without any sinus rhythm ECGs were excluded. AP location was determined based on electrophysiological testing during catheter ablation and categorized into right-sided, septal and left-sided APs. Multi-task learning with auxiliary identification of the presence of pre-excitation, parahisian pathways and locations where a transseptal puncture is potentially required was used to increase usability and performance. The DNN was compared to the Milstein and Arruda algorithms.
Results
Between 1997 and 2023, 645 patients who underwent catheter ablation for an AP were included in the study. The model was developed using 1.394 ECGs from 567 patients. The DNN was tested using 78 ECGs in two independent cohorts. The model outperformed both the Milstein and Arruda algorithms with an area under the receiver operating characteristic curve (AUROC) of .92 (95% confidence interval: .88–.96) compared to the Arruda algorithm (AUROC of .80; p <.001) and the Milstein algorithm (AUROC of .81; p <.001).
Conclusions
Our model showed excellent discriminatory performance in predicting the location of an accessory pathway while outperforming conventional techniques. Clinically, this tool can improve preoperative planning and risk stratification.
期刊介绍:
EJCI considers any original contribution from the most sophisticated basic molecular sciences to applied clinical and translational research and evidence-based medicine across a broad range of subspecialties. The EJCI publishes reports of high-quality research that pertain to the genetic, molecular, cellular, or physiological basis of human biology and disease, as well as research that addresses prevalence, diagnosis, course, treatment, and prevention of disease. We are primarily interested in studies directly pertinent to humans, but submission of robust in vitro and animal work is also encouraged. Interdisciplinary work and research using innovative methods and combinations of laboratory, clinical, and epidemiological methodologies and techniques is of great interest to the journal. Several categories of manuscripts (for detailed description see below) are considered: editorials, original articles (also including randomized clinical trials, systematic reviews and meta-analyses), reviews (narrative reviews), opinion articles (including debates, perspectives and commentaries); and letters to the Editor.