一个用于动作级显微吻合训练与表现反馈的集成视频-人工智能平台
An Integrated Video-AI Platform for Action-Level Microanastomosis Training and Performance Feedback
AI总结：
提出集成视频-人工智能平台，通过动作分割、器械跟踪和语言模型反馈，实现显微吻合训练的可扩展动作级表现评估与交互式反馈。
AI中文摘要：
培养显微吻合技能需要反复练习并及时获得针对具体动作的反馈，然而专家审阅冗长的显微镜视频无法扩展到频繁或分散式训练。我们提出了一个集成视频-人工智能平台，通过三个相互连接的模块将完整的模拟手术过程转化为可检查、可交互的反馈。首先，所提出的变换器将视频分割为六个手术动作。其次，目标检测与跟踪在每个动作内定位器械尖端；由此产生的运动学特征和动作统计驱动五个符合NOMAT标准的表现维度的监督分类。第三，一个基于基础的大型语言模型（LLM）利用这些结构化输出，通过统一界面回答用户关于当前场景、动作、运动和预测表现的问题。在一项两中心研究中，17名参与者完成了72个手术流程，共包含576次缝合放置。动作分割模块达到了87.66%的准确率和82.86%的F1分数，经过工作流感知优化后分别提高到93.62%和88.32%。五个表现分类器实现了76.0%的平均准确率，Cohen's κ值从0.63到0.93。尽管语言界面和教育益处需要前瞻性评估，这些结果确立了专家监督平台的技术基础，该平台可以缩短审阅时间、揭示表现评估背后的证据，并支持可扩展的形成性显微外科训练。
英文摘要：
Developing microanastomosis skill requires repeated practice with timely, action-specific feedback, yet expert review of lengthy microscope videos does not scale to frequent or distributed training. We present an integrated video-AI platform that turns a complete simulated procedure into inspectable, interactive feedback through three connected modules. First, a proposed transformer segments the video into six surgical actions. Second, object detection and tracking localize instrument tips within each action; the resulting kinematic features and action statistics drive supervised classification of five NOMAT-aligned performance dimensions. Third, a grounded large language model (LLM) uses these structured outputs to answer user questions about the current scene, actions, motion, and predicted performance through a unified interface. In a two-site study, 17 participants completed 72 procedures comprising 576 suture placements. The action-segmentation module achieved 87.66\% accuracy and 82.86\% F1, increasing to 93.62\% and 88.32\% after workflow-aware refinement. The five performance classifiers achieved 76.0\% mean accuracy, with Cohen's $κ$ from 0.63 to 0.93. Although the language interface and educational benefit require prospective evaluation, these results establish the technical basis for an expert-supervised platform that can shorten review, expose the evidence behind performance estimates, and support scalable formative microsurgical training.
