一种利用深度学习预测心脏再同步治疗反应的新方法
A new method using deep learning to predict the response to cardiac resynchronization therapy
AI总结：
本研究提出一种结合SPECT MPI极坐标图与临床及ECG特征的深度学习模型，用于预测CRT反应，在218例患者中AUC达0.83，优于机器学习模型和指南标准。
摘要：
背景。从门控单光子发射计算机断层扫描心肌灌注显像（SPECT MPI）测量的临床参数在预测心脏再同步治疗（CRT）患者结局方面具有价值，但仍显示出局限性。本研究旨在通过深度学习（DL）结合临床变量、心电图（ECG）特征、心脏功能评估参数以及门控SPECT MPI的极坐标图来预测CRT反应。方法。本研究纳入218例接受静息门控SPECT MPI检查的患者。CRT反应定义为6个月随访时左心室射血分数（LVEF）增加>5%。通过结合预训练的VGG16模块和多层感知器构建了DL模型。模型输入两种模态的数据：来自SPECT MPI的极坐标图图像和来自临床特征及ECG参数的表格数据。将梯度加权类激活映射（Grad-CAM）应用于VGG16模块，以提供极坐标图的可解释性。为了比较，仅使用表格特征训练了四种机器学习（ML）模型。结果。对218例接受CRT植入且反应率为55.5%（n=121）的患者进行了建模。DL模型表现出平均AUC（0.83）、准确率（0.73）、灵敏度（0.76）和特异度（0.69），优于ML模型和指南标准。指南建议的准确率（0.53）、灵敏度（0.75）和特异度（0.26）。结论。DL模型优于ML模型，展示了利用SPECT MPI极坐标图的额外预测益处。以医学图像形式直接纳入额外患者数据可以改善CRT反应预测。
英文摘要：
Background. Clinical parameters measured from gated single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) have value in predicting cardiac resynchronization therapy (CRT) patient outcomes, but still show limitations. The purpose of this study is to combine clinical variables, features from electrocardiogram (ECG), and parameters from assessment of cardiac function with polarmaps from gated SPECT MPI through deep learning (DL) to predict CRT response. Methods. 218 patients who underwent rest gated SPECT MPI were enrolled in this study. CRT response was defined as an increase in left ventricular ejection fraction (LVEF) > 5% at a 6-month follow up. A DL model was constructed by combining a pre-trained VGG16 module and a multilayer perceptron. Two modalities of data were input to the model: polarmap images from SPECT MPI and tabular data from clinical features and ECG parameters. Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to the VGG16 module to provide explainability for the polarmaps. For comparison, four machine learning (ML) models were trained using only the tabular features. Results. Modeling was performed on 218 patients who underwent CRT implantation with a response rate of 55.5% (n = 121). The DL model demonstrated average AUC (0.83), accuracy (0.73), sensitivity (0.76), and specificity (0.69) surpassing the ML models and guideline criteria. Guideline recommendations presented accuracy (0.53), sensitivity (0.75), and specificity (0.26). Conclusions. The DL model outperformed the ML models, showcasing the additional predictive benefit of utilizing SPECT MPI polarmaps. Incorporating additional patient data directly in the form of medical imagery can improve CRT response prediction.
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