Related Experiment Video
Updated: May 5, 2026
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
Published on: June 18, 2020
Interpretable machine learning models based on shear-wave elastography radiomics for predicting cardiovascular
Ruihong Dai1, Miaomiao Sun1, Mei Lu1
1Department of Ultrasound, Meng Cheng County Hospital of Chinese Medicine, Bozhou City, Anhui Province, China.
Insights
Diabetic kidney disease patients face high cardiovascular disease risk. A machine learning model combining clinical data and shear wave elastography radiomics effectively predicts this risk for early intervention.
Area of Science:
- Nephrology
- Cardiology
- Radiology
- Artificial Intelligence
Background:
- Diabetic kidney disease (DKD) significantly increases cardiovascular disease (CVD) risk.
- Early prediction and management of CVD risk factors in DKD patients are crucial for improved outcomes.
Purpose of the Study:
- Develop and validate machine learning (ML) models for CVD risk prediction in DKD patients.
- Integrate clinical data with shear wave elastography (SWE) radiomics features.
Main Methods:
- Retrospective analysis of 586 DKD patients.
- Development of ML models using clinical data and SWE radiomics features.
- Internal and external validation of models, including Support Vector Machine (SVM) and SHAP analysis.
Main Results:
- 30.7% of patients were identified at risk for CVD.
- Six radiomics features and six clinical data points were significant predictors.
- The SVM model demonstrated superior performance in both internal and external validations.
Conclusions:
- An SVM model integrating clinical and radiomics data effectively predicts CVD risk in DKD patients.
- This approach facilitates early CVD risk identification and timely interventions.
Background:
The risk of cardiovascular complications is significantly elevated in patients with diabetic kidney disease (DKD). Recognizing the link between the progression of DKD and an increased risk of cardiovascular disease (CVD), it is crucial to focus on the early prediction and management of CVD risk factors among these patients to potentially enhance their health outcomes.
Objective:
This study sought to bridge the existing gap by developing and validating machine learning (ML) models that utilize clinical data and shear wave elastography (SWE) radiomics features to identify patients at risk of CVD, ultimately aiming to improve the management of DKD.
Materials And Methods:
This study conducted a retrospective analysis of 586 patients with DKD, dividing them into training and external validation cohorts. We categorized patients based on the presence or absence of CVD. Utilizing SWE imaging, we extracted and standardized radiomics features to develop multiple ML models. These models underwent internal validation using radiomics features alone, clinical data, or a combination thereof. The optimal model was then identified, and its feature importance was assessed through the Shapley Additive Explanations (SHAP) method, before proceeding to external validation.
Results:
Among the 586 patients analyzed, 30.7% (180/586) were identified as at risk for CVD. The study pinpointed six significant radiomics features related to CVD, alongside six critical pieces of clinical data. The Support Vector Machine (SVM) model outperformed others in both internal and external validations. Further, SHAP analysis highlighted five principal determinants of CVD risk, comprising three clinical indicators and two SWE radiomics features.
Conclusions:
This study highlights the effectiveness of an SVM model that combines clinical and radiomics features in predicting CVD risk among DKD patients. It enables early prediction of CVD in this patient group, thereby supporting the implementation of timely and suitable interventions.
