Machine Learning Models Integrating Two-Dimensional Speckle Tracking Echocardiography and Clinical Variables for Diagnosis of Severe Coronary Artery Disease.
To develop and validate machine learning (ML) models integrating two-dimensional speckle tracking echocardiography (2D-STE) parameters with clinical variables for robust identification of severe coronary artery disease (sCAD).
In this retrospective cohort study, five distinct ML models (Random Forest [RF], Support Vector Machine [SVM], K-Nearest Neighbors [KNN], Multi-Layer Perceptron [MLP], and Extremely Randomized Trees [Extra Trees]) were constructed to identify sCAD on a cohort of 204 patients (80% training set, 20% independent test set). Within the independent test set, two junior sonographers' diagnostic performance for sCAD was compared first without and then with ML assistance over a 2-week interval. SHapley Additive exPlanations (SHAP) analysis was applied to visualize and interpret the models, identifying key features driving sCAD prediction accuracy, with results visualized through dependence diagrams and force plot. Furthermore, a clinical nomogram integrating key predictors identified by ML models was developed to enable individualized quantification of sCAD risk.
Utilizing five features, the MLP demonstrated the best performance with an area under the curve (AUC) of 0.870 and a sensitivity of 0.944. The SHAP visualization analysis for this model indicated that "LV AP4 Endo Peak L. Time SD" significantly influenced its predictions. The MLP model (AUC = 0.870) outperformed both junior sonographers (AUC = 0.687) and a nomogram constructed from ML-selected features (AUC = 0.712). Additionally, the results revealed that junior sonographers achieved significantly improved performance when assisted by the ML models.
The developed ML models could differentiate patients with angiography-confirmed sCAD from those without. Importantly, these models significantly improved the diagnostic performance of junior sonographers when used as an assistive tool.
In this retrospective cohort study, five distinct ML models (Random Forest [RF], Support Vector Machine [SVM], K-Nearest Neighbors [KNN], Multi-Layer Perceptron [MLP], and Extremely Randomized Trees [Extra Trees]) were constructed to identify sCAD on a cohort of 204 patients (80% training set, 20% independent test set). Within the independent test set, two junior sonographers' diagnostic performance for sCAD was compared first without and then with ML assistance over a 2-week interval. SHapley Additive exPlanations (SHAP) analysis was applied to visualize and interpret the models, identifying key features driving sCAD prediction accuracy, with results visualized through dependence diagrams and force plot. Furthermore, a clinical nomogram integrating key predictors identified by ML models was developed to enable individualized quantification of sCAD risk.
Utilizing five features, the MLP demonstrated the best performance with an area under the curve (AUC) of 0.870 and a sensitivity of 0.944. The SHAP visualization analysis for this model indicated that "LV AP4 Endo Peak L. Time SD" significantly influenced its predictions. The MLP model (AUC = 0.870) outperformed both junior sonographers (AUC = 0.687) and a nomogram constructed from ML-selected features (AUC = 0.712). Additionally, the results revealed that junior sonographers achieved significantly improved performance when assisted by the ML models.
The developed ML models could differentiate patients with angiography-confirmed sCAD from those without. Importantly, these models significantly improved the diagnostic performance of junior sonographers when used as an assistive tool.
Authors
Hu Hu, Fu Fu, Zeng Zeng, Teng Teng, Luo Luo, Ying Ying, Deng Deng, Yang Yang, Ren Ren
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