用视频分类与大模型结合，实现超声视频的智能诊断。
Auto-US: An Ultrasound Video Diagnosis Agent Using Video Classification Framework and LLMs
- 融合超声视频与临床文本，构建端到端诊断框架。
- 在495个视频数据集上达到86.73%分类准确率。
- 生成可解释诊断建议，临床医生验证评分超3分/5。
AI辅助超声视频诊断为提升医学影像分析效率和准确性带来新机遇，但现有研究在数据集多样性、诊断性能和临床可用性方面仍受限。本文提出Auto-US，一种集成超声视频与临床诊断文本的智能诊断代理。为此，我们构建了包含495个视频、覆盖五个类别和三个器官的公开数据集CUV Dataset，数据来源自多个开放资源。开发了CTU-Net模型，在超声视频分类任务中达到86.73%的准确率，表现优于现有方法。通过引入大语言模型，Auto-US能生成具有临床意义的诊断建议，每个病例的最终诊断评分均超过5分制中的3分，且经专业医生验证。结果表明，Auto-US在真实超声应用中具备有效性和临床潜力。代码与数据已开源：https://github.com/Bean-Young/Auto-US。
原文摘要 · Abstract (English)
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 \textbf{Auto-US}, an intelligent diagnosis agent that integrates ultrasound video data with clinical diagnostic text. To support this, we constructed \textbf{CUV Dataset} of 495 ultrasound videos spanning five categories and three organs, aggregated from multiple open-access sources. We developed \textbf{CTU-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.
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