Key result
Deep neural networks trained on synthetic longitudinal strain data predict CRT response with ~90% accuracy.
Why the study?
Few research works have used deep learning to predict cardiac resynchronization therapy response in heart failure patients.
Can deep learning models using two-dimensional echocardiographic strain traces accurately predict CRT response in heart failure patients?
Observational (n=131)
Can deep learning models using two-dimensional echocardiographic strain traces accurately predict CRT response in heart failure patients?
Deep learning models using echocardiographic strain data can accurately predict CRT response in heart failure patients, potentially serving as a clinical auxiliary tool.
DL models on synthetic strain data predict CRT response with high accuracy; hypothesis-generating and requires prospective real-world validation before any clinical use.
BACKGROUND: Recently, as a relatively novel technology, artificial intelligence (especially in the deep learning fields) has received more and more attention from researchers and has successfully been applied to many biomedical domains. Nonetheless, just a few research works use deep learning skills to predict the cardiac resynchronization therapy (CRT)-response of heart failure patients. OBJECTIVE: We try to use the deep learning-based technique to construct a model which is used to predict the CRT response of patients with high prediction accuracy, precision, and sensitivity. METHODS: Using two-dimensional echocardiographic strain traces from 131 patients, we pre-processed the data and synthesized 2000 model inputs through the synthetic minority oversampling technique (SMOTE). These inputs trained and optimized deep neural networks (DNN) and one-dimensional convolution neural networks (1D-CNN). Visualization of prediction results was performed using t-distributed stochastic neighbor embedding (t-SNE), and model performance was evaluated using accuracy, precision, sensitivity, F1 score, and specificity. Variable importance was assessed using Shapley additive explanations (SHAP) analysis. RESULTS: Both the optimal DNN and 1D-CNN models demonstrated exceptional predictive performance, with prediction accuracy, precision, and sensitivity all around 90%. Furthermore, the area under the receiver operating characteristic curve (AUROC) of the optimal 1D-CNN and DNN models achieved 0.8734 and 0.9217, respectively. Crucially, the most significant input variables for both models align well with clinical experience, further corroborating their robustness and applicability in real-world settings. CONCLUSIONS: We believe that both the DL models could be an auxiliary to help in treatment response prediction for doctors because of the excellent prediction performance and the convenience of obtaining input data to predict the CRT response of patients clinically.
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Chang et al. (2024) conducted an observational in Heart failure (n=131). Deep learning models (DNN and 1D-CNN) was evaluated on CRT response prediction accuracy, precision, sensitivity, and AUROC. Deep neural network and 1D-CNN models trained on synthetic longitudinal strain data predicted cardiac resynchronization therapy response with ~90% accuracy and AUROCs of 0.9217 and 0.8734.
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