Auto-US: An Ultrasound Video Diagnosis Agent Using Video Classification Framework and LLMs

arXiv:2511.07748v1 Announce Type: cross
Abstract: AI-assisted ultrasound video diagnosis presents new opportunities to enhance the efficiency and accuracy of medical imaging analysis. However, existing research remains limited in terms of dataset diversity, diagnostic performance, and clinical applicability. In this study, we propose textbfAuto-US, an intelligent diagnosis agent that integrates ultrasound video data with clinical diagnostic text. To support this, we constructed textbfCUV Dataset of 495 ultrasound videos spanning five categories and three organs, aggregated from multiple open-access sources. We developed textbfCTU-Net, which achieves state-of-the-art performance in ultrasound video classification, reaching an accuracy of 86.73% Furthermore, by incorporating large language models, Auto-US is capable of generating clinically meaningful diagnostic suggestions. The final diagnostic scores for each case exceeded 3 out of 5 and were validated by professional clinicians. These results demonstrate the effectiveness and clinical potential of Auto-US in real-world ultrasound applications. Code and data are available at: https://github.com/Bean-Young/Auto-US.