Doldi Florian, Plagwitz Lucas, Hoffmann Lea Philine, Rath Benjamin, Frommeyer Gerrit, Reinke Florian, Leitz Patrick, Büscher Antonius, Güner Fatih, Brix Tobias, Wegner Felix Konrad, Willy Kevin, Hanel Yvonne, Dittmann Sven, Haverkamp Wilhelm, Schulze-Bahr Eric, Varghese Julian, Eckardt Lars
Department for Cardiology II-Electrophysiology, University Hospital Münster, 48149 Münster, Germany.
Institute of Medical Informatics, University of Münster, 48149 Münster, Germany.
J Pers Med. 2022 Jul 13;12(7):1135. doi: 10.3390/jpm12071135.
The long-QT syndrome (LQTS) is the most common ion channelopathy, typically presenting with a prolonged QT interval and clinical symptoms such as syncope or sudden cardiac death. Patients may present with a concealed phenotype making the diagnosis challenging. Correctly diagnosing at-risk patients is pivotal to starting early preventive treatment.
Identification of congenital and often concealed LQTS by utilizing novel deep learning network architectures, which are specifically designed for multichannel time series and therefore particularly suitable for ECG data.
A retrospective artificial intelligence (AI)-based analysis was performed using a 12-lead ECG of genetically confirmed LQTS ( = 124), including 41 patients with a concealed LQTS (33%), and validated against a control cohort ( = 161 of patients) without known LQTS or without QT-prolonging drug treatment but any other cardiovascular disease. The performance of a fully convolutional network (FCN) used in prior studies was compared with a different, novel convolutional neural network model (XceptionTime). We found that the XceptionTime model was able to achieve a higher balanced accuracy score (91.8%) than the associated FCN metric (83.6%), indicating improved prediction possibilities of novel AI architectures. The predictive accuracy prevailed independently of age and QT parameters.
In this study, the XceptionTime model outperformed the FCN model for LQTS patients with even better results than in prior studies. Even when a patient cohort with cardiovascular comorbidities is used. AI-based ECG analysis is a promising step for correct LQTS patient identification, especially if common diagnostic measures might be misleading.
长QT综合征（LQTS）是最常见的离子通道病，通常表现为QT间期延长以及晕厥或心源性猝死等临床症状。患者可能呈现隐匿性表型，这使得诊断具有挑战性。正确诊断高危患者对于尽早开始预防性治疗至关重要。
利用专门为多通道时间序列设计、因此特别适用于心电图数据的新型深度学习网络架构，识别先天性且常为隐匿性的LQTS。
使用经基因确诊的LQTS患者（n = 124）的12导联心电图进行基于人工智能（AI）的回顾性分析，其中包括41例隐匿性LQTS患者（33%），并与一个无已知LQTS或未接受延长QT药物治疗但患有任何其他心血管疾病的对照队列（n = 161例患者）进行验证。将先前研究中使用的全卷积网络（FCN）的性能与另一种不同的新型卷积神经网络模型（XceptionTime）进行比较。我们发现，XceptionTime模型能够获得比相关FCN指标（83.6%）更高的平衡准确率得分（91.8%），这表明新型AI架构的预测可能性有所提高。预测准确性不受年龄和QT参数的影响。
在本研究中，对于LQTS患者，XceptionTime模型优于FCN模型，且结果比先前研究更好。即使使用患有心血管合并症的患者队列也是如此。基于AI的心电图分析是正确识别LQTS患者的一个有前景的步骤，特别是在常见诊断措施可能产生误导的情况下。
