Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Heart Center and Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
J Cardiovasc Electrophysiol. 2024 Sep;35(9):1849-1858. doi: 10.1111/jce.16373. Epub 2024 Jul 25.
We aimed to construct an artificial intelligence-enabled electrocardiogram (ECG) algorithm that can accurately predict the presence of left atrial low-voltage areas (LVAs) in patients with persistent atrial fibrillation.
The study included 587 patients with persistent atrial fibrillation who underwent catheter ablation procedures between March 2012 and December 2023 and 942 scanned images of 12-lead ECGs obtained before the ablation procedures were performed. Artificial intelligence-based algorithms were used to construct models for predicting the presence of LVAs. The DR-FLASH and APPLE clinical scores for LVA prediction were calculated. We used a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis to evaluate model performance.
The data obtained from the participants were split into training (n = 469), validation (n = 58), and test sets (n = 60). LVAs were detected in 53.7% of all participants. Using ECG alone, the deep learning algorithm achieved an area under the ROC curve (AUROC) of 0.752, outperforming both the DR-FLASH score (AUROC = 0.610) and the APPLE score (AUROC = 0.510). The random forest classification model, which integrated a probabilistic deep learning model and clinical features, showed a maximum AUROC of 0.759. Moreover, the ECG-based deep learning algorithm for predicting extensive LVAs achieved an AUROC of 0.775, with a sensitivity of 0.816 and a specificity of 0.896. The random forest classification model for predicting extensive LVAs achieved an AUROC of 0.897, with a sensitivity of 0.862, and a specificity of 0.935.
The deep learning model based exclusively on ECG data and the machine learning model that combined a probabilistic deep learning model and clinical features both predicted the presence of LVAs with a higher degree of accuracy than the DR-FLASH and the APPLE risk scores.
我们旨在构建一种基于人工智能的心电图（ECG）算法，以准确预测持续性心房颤动患者左心房低电压区（LVA）的存在。
本研究纳入了 2012 年 3 月至 2023 年 12 月期间接受导管消融术的 587 例持续性心房颤动患者，以及消融术前获得的 12 导联 ECG 扫描图像 942 幅。基于人工智能的算法用于构建预测 LVA 存在的模型。计算 LVA 预测的 DR-FLASH 和 APPLE 临床评分。我们使用接受者操作特征（ROC）曲线、校准曲线和决策曲线分析来评估模型性能。
参与者的数据分为训练集（n=469）、验证集（n=58）和测试集（n=60）。所有参与者中 LVA 的检出率为 53.7%。仅使用 ECG，深度学习算法的 ROC 曲线下面积（AUROC）为 0.752，优于 DR-FLASH 评分（AUROC=0.610）和 APPLE 评分（AUROC=0.510）。集成概率深度学习模型和临床特征的随机森林分类模型的最大 AUROC 为 0.759。此外，用于预测广泛 LVA 的基于 ECG 的深度学习算法的 AUROC 为 0.775，敏感性为 0.816，特异性为 0.896。用于预测广泛 LVA 的随机森林分类模型的 AUROC 为 0.897，敏感性为 0.862，特异性为 0.935。
仅基于 ECG 数据的深度学习模型和结合概率深度学习模型和临床特征的机器学习模型预测 LVA 的准确性均高于 DR-FLASH 和 APPLE 风险评分。
