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Updated: Jan 9, 2026
Ultrasonic Assessment of Myocardial Microstructure
Published on: January 14, 2014
Echocardiography Combined With Radiomics and Deep Transfer Learning to Diagnose Hypertrophic Cardiomyopathy and Other
Jiangtao Wang1, Sensen Wang2, Tao Yu3
1Department of Ultrasound Medicine, The First Affiliated Hospital of Wannan Medical College, 241001 Wuhu, Anhui, China.
Insights
A new fusion model combining radiomics and deep transfer learning (DTL) effectively differentiates hypertrophic cardiomyopathy (HCM) from other causes of left ventricular hypertrophy (LVH) using echocardiography. This approach offers improved diagnostic accuracy and clinical utility for distinguishing these cardiac conditions.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Differentiating hypertrophic cardiomyopathy (HCM) from left ventricular hypertrophy (LVH) due to other causes using transthoracic echocardiography (TTE) is challenging.
- Radiomics and deep transfer learning (DTL) show promise for cardiac diagnosis.
- No prior studies have combined radiomics and DTL for differentiating HCM from other LVH causes.
Purpose of the Study:
- To develop and validate a fusion model integrating radiomic and DTL features from TTE for improved differentiation of HCM from other LVH causes.
- To provide more reliable diagnostic support for distinguishing between HCM and other forms of LVH.
Main Methods:
- A multicenter study included 971 patients (303 HCM, 668 other LVH).
- Radiomic features were extracted using pyradiomics, and DTL features were obtained via DenseNet121.
- A support vector machine (SVM) classifier was used with features selected by LASSO; model performance was evaluated using ROC curves and DCA.
Main Results:
- The fusion model achieved high diagnostic performance with AUC values of 0.966 (training), 0.945 (internal validation), and 0.934 (external validation).
- The fusion model outperformed models using only radiomic or DTL features.
- Decision curve analysis (DCA) demonstrated superior clinical effectiveness compared to two ultrasound physicians.
Conclusions:
- A fusion model combining radiomics and DTL features significantly enhances the ability to distinguish HCM from other causes of LVH.
- This integrated approach shows strong potential for clinical application in cardiac diagnosis.
Background:
Hypertrophic cardiomyopathy (HCM) and left ventricular hypertrophy (LVH) from other causes present similar features on transthoracic echocardiography (TTE), making an accurate differentiation challenging. Recent advancements in radiomics and deep transfer learning (DTL) have shown promise; however, no studies have combined these techniques to diagnose HCM and LVH resulting from other causes. Therefore, we developed a fusion model that integrates radiomic features from the left ventricular myocardium in the four-chamber view of TTE with DTL features to differentiate HCM from other causes of LVH, providing more reliable diagnostic support.
Methods:
This multicenter study included 971 patients (303 with HCM, 668 with hypertensive heart disease and uremic cardiomyopathy). Patients from Institution 1 were split into a training set and an internal validation set, while patients from Institution 2 served as an external validation set. Radiomic features were extracted using pyradiomics, and DTL features were obtained via DenseNet121. Features were selected using least absolute shrinkage and selection operator (LASSO) and input into ten machine learning algorithms, with support vector machine (SVM) as the classifier. Model performance was assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA) and compared with the diagnostic results of two ultrasound physicians.
Results:
The fusion model demonstrated excellent diagnostic performance: the area under the curve (AUC) values were 0.966 (training set), 0.945 (internal validation), and 0.934 (external validation), thereby outperforming models that used only radiomic or DTL features. DCA indicated superior clinical effectiveness, surpassing the diagnostic performance of two ultrasound physicians.
Conclusions:
A fusion model combining radiomics and DTL features significantly improves the ability to distinguish HCM from other causes of LVH and has strong potential for clinical applications.
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