短QT综合征(SQTS)是一种遗传性心脏离子通道疾病,与年轻和其他健康个体的心脏猝死(SCD)风险增加有关。SCD通常是SQTS患者的第一个临床表现。然而,心律失常危险分层目前在无症状患者中是不能令人满意的。在这种情况下,基于人工智能的心电图(ECG)分析从未用于改善SQTS患者的风险分层.这项研究的目的是借助不同的AI算法分析SQTS患者的心电图,以评估他们区分有和没有记录的危及生命的心律失常事件的能力。研究组包括104例SQTS患者,其中37人在就诊时和/或随访期间有明显的心律失常事件。由三位心脏病专家独立测量了13种心电图特征;然后,将数据集随机分为三个子集(训练,验证,和测试)。训练了五个浅层神经网络,已验证,并测试使用不同的ECG特征子集来预测受试者特定类别(非事件/事件)。此外,几种深度学习和机器学习算法,如视觉变压器,双变压器,MobileNetV3,EfficientNetV2,ConvNextTiny,胶囊网络,并对逻辑回归进行了训练,已验证,并直接在扫描的心电图图像上进行测试,无需任何手动特征提取。此外,一个浅层的神经网络,一维变压器分类器,训练了一个一维CNN,已验证,并对从上述扫描图像中提取的ECG信号进行测试。分类指标通过灵敏度进行评估,特异性,阳性和阴性预测值,准确度,和曲线下的面积。结果证明,人工智能可以帮助临床医生更好地对SQTS患者的心律失常风险进行分层。特别是,浅层神经网络处理功能在识别不会遭受潜在致命事件的患者方面表现最佳。这可以为这组患者的基于心电图的风险分层铺平道路,可能有助于拯救年轻人和其他健康个体的生命。
Short QT syndrome (SQTS) is an inherited cardiac ion-channel disease related to an increased risk of sudden cardiac death (SCD) in young and otherwise healthy individuals. SCD is often the first clinical presentation in patients with SQTS. However, arrhythmia risk stratification is presently unsatisfactory in asymptomatic patients. In this context, artificial intelligence-based electrocardiogram (ECG) analysis has never been applied to refine risk stratification in patients with SQTS. The purpose of this study was to analyze ECGs from SQTS patients with the aid of different AI algorithms to evaluate their ability to discriminate between subjects with and without documented life-threatening arrhythmic events. The study group included 104 SQTS patients, 37 of whom had a documented major arrhythmic event at presentation and/or during follow-up. Thirteen ECG features were measured independently by three expert cardiologists; then, the dataset was randomly divided into three subsets (training, validation, and testing). Five shallow neural networks were trained, validated, and tested to predict subject-specific class (non-event/event) using different subsets of ECG features. Additionally, several deep learning and machine learning algorithms, such as Vision Transformer, Swin Transformer, MobileNetV3, EfficientNetV2, ConvNextTiny, Capsule Networks, and logistic regression were trained, validated, and tested directly on the scanned ECG images, without any manual feature extraction. Furthermore, a shallow neural network, a 1-D transformer classifier, and a 1-D CNN were trained, validated, and tested on ECG signals extracted from the aforementioned scanned images. Classification metrics were evaluated by means of sensitivity, specificity, positive and negative predictive values, accuracy, and area under the curve. Results prove that artificial intelligence can help clinicians in better stratifying risk of arrhythmia in patients with SQTS. In particular, shallow neural networks\' processing features showed the best performance in identifying patients that will not suffer from a potentially lethal event. This could pave the way for refined ECG-based risk stratification in this group of patients, potentially helping in saving the lives of young and otherwise healthy individuals.