An interpretable machine learning model for classifying cerebral small vessel disease using carotid plaque ultrasound radiomics and clinical features

We developed an interpretable machine learning model to classify cerebral small vessel disease (CSVD) using carotid plaque ultrasound radiomics combined with clinical features. In 193 participants (117 with CSVD, 76 controls), 107 ultrasound features were extracted, and 7 key features were selected via LASSO regression. Multimodal models significantly outperformed clinical-only models (p < 0.05), with the best model achieving an AUC of 0.888. SHAP analysis provided model interpretability, and ROC/decision curve analysis confirmed its robustness. This model offers a promising cost-effective CSVD screening tool for low-resource settings where MRI is limited. Further multicenter validation is recommended to enhance generalizability.