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.

使用深度学习模型将临床参数、心电图特征、心脏功能评估参数和 SPECT MPI 的极地图相结合，预测心脏重同步治疗的患者反应，对于预测预后具有较高准确性。