Abstract The clinical practice of archiving static 2D images from dynamic breast ultrasound (BUS) examinations loses vital temporal information, limiting computer-aided diagnosis systems. We challenge this constraint by proposing a novel framework that computationally recovers lost temporal dynamics to enhance diagnostic accuracy. Our framework first synthesizes a BUS video from a static key frame using a purpose-built generative model. We introduce a key-frame conditioning strategy to ensure the anatomical fidelity of the lesion is preserved while generating useful dynamic cues. Subsequently, the high-fidelity static image and the synthesized video are fed into Image Video network(IV-Net), a dual-branch fusion network that integrates pristine spatial details with recovered temporal context for robust classification. Evaluated on internal and external datasets, our framework outperforms methods relying solely on static images, achieving AUC of 94.13% and 82.55%, respectively. Furthermore, a reader study indicates the generated videos are indistinguishable to experts and improve diagnostic performance. Overall, our method demonstrates generative model potential to restore lost information, paving the way for reliable BUS diagnostics. Similar content being viewed by others Article PDF Introduction Breast cancer is the most prevalent and threatening disease in females1. Ultrasound imaging is one of the most widely used screening techniques for breast cancer. Radiologists use a transducer across the tissue to observe lesion morphology, echogenicity, and deformation from different points of view. Thus, diagnosing with breast ultrasound (BUS) inherently involves dynamic imaging. This spatio-temporal information is used by clinicians to distinguish the possibility of malignant and benign lesions. Unfortunately, in clinical practice, only a few representative static 2D frames are usually archived in a patient’s health records, while the dynamic video is discarded2. The Automated Breast Ultrasound (ABUS) can automatically capture full volumetric data blocks3, but this technology is still not prevalent in most institutions. Within the domain of computer-aided breast ultrasound