This study aimed to develop a predictive model using deep-learning (DL)-based three-dimensional (3D) super-resolution ultrasound images for the diagnosis of benign and malignant breast lesions. Eight models, including four deep learning models and four machine learning models, were developed based on original and super-resolution images. The best performing model was SRMobileNet_v2, which was used to construct a nomogram integrating clinical factors. The performance of nomogram was evaluated using receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and calibration curves. The deep-learning nomogram was constructed using the SRMobileNet_v2 model score, tumor size, and patient age, resulting in superior predictive efficacy compared to the nomogram without the SRMobileNet_v2 model score.
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