构建首个面向超声操作流程的视频问答基准，助力智能超声系统发展
ReXSonoVQA: A Video QA Benchmark for Procedure-Centric Ultrasound Understanding
- 设计视频QA数据集，聚焦操作目标推理、伪影处理与流程规划三类能力
- 514段视频+514题测试，发现大模型在故障诊断上仍远落后于文本基线
- 适合医学AI、机器人辅助超声及临床训练系统研究者使用
超声采集依赖熟练的探头操作与实时调整。视觉语言模型（VLMs）有望实现自主超声系统，但现有基准仅评估静态图像，缺乏对动态流程理解的评测。我们提出ReXSonoVQA，一个包含514个视频片段和514个问题（249道多选题，265道开放题）的视频问答基准，针对三大能力：动作-目标推理、伪影识别与优化、流程上下文与规划。对Gemini 3 Pro、Qwen3.5-397B、LLaVA-Video-72B和Seed 2.0 Pro的零样本评估显示，尽管模型可提取部分流程信息，但在故障排查类问题上表现不佳，改进微弱，暴露出因果推理能力的局限性。ReXSonoVQA为超声教学、引导与机器人自动化系统的感知能力提升提供评测基础。
原文摘要 · Abstract (English)
Ultrasound acquisition requires skilled probe manipulation and real-time adjustments. Vision-language models (VLMs) could enable autonomous ultrasound systems, but existing benchmarks evaluate only static images, not dynamic procedural understanding. We introduce ReXSonoVQA, a video QA benchmark with 514 video clips and 514 questions (249 MCQ, 265 free-response) targeting three competencies: Action-Goal Reasoning, Artifact Resolution & Optimization, and Procedure Context & Planning. Zero-shot evaluation of Gemini 3 Pro, Qwen3.5-397B, LLaVA-Video-72B, and Seed 2.0 Pro shows VLMs can extract some procedural information, but troubleshooting questions remain challenging with minimal gains over text-only baselines, exposing limitations in causal reasoning. ReXSonoVQA enables developing perception systems for ultrasound training, guidance, and robotic automation.
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