OBJECTIVE: BRAF MATERIALS AND METHODS: Regions of interest (ROI) were manually annotated in the ultrasound images, and radiomic and DTL features were extracted. These were used in a joint DTL-radiomics (DTLR) model. Fourteen DTL models were employed, and feature selection was performed using the LASSO regression. Eight machine learning methods were used to construct predictive models. Model performance was primarily evaluated using area under the curve (AUC), accuracy, sensitivity and specificity. The interpretability of the model was visualized using gradient-weighted class activation maps (Grad-CAM). RESULTS: Sole reliance on radiomics for identification of BRAF CONCLUSION: The ResNet152-based DTLR model demonstrated significant value in identifying BRAF