Abstract
Proficiency in microanastomosis is a critical surgical skill in neurosurgery, where the ability to precisely manipulate fine instruments is crucial to successful outcomes. These procedures require sustained attention, coordinated hand movements, and highly refined motor skills, underscoring the need for objective and systematic methods to evaluate and enhance microsurgical training. Conventional assessment approaches typically rely on expert raters supervising the procedures or reviewing surgical videos, which is an inherently subjective process prone to inter-rater variability, inconsistency, and significant time investment. These limitations highlight the necessity for automated and scalable solutions. To address this challenge, we introduce a novel AI-driven framework for automated action segmentation and performance assessment in microanastomosis procedures, designed to operate efficiently on edge computing platforms. The proposed system comprises three main components: (1) an object tip tracking and localization module based on YOLO and DeepSORT; (2) an action segmentation module leveraging self-similarity matrix for action boundary detection and unsupervised clustering; and (3) a supervised classification module designed to evaluate surgical gesture proficiency. Experimental validation on a dataset of 58 expert-rated microanastomosis videos demonstrates the effectiveness of our approach, achieving a frame-level action segmentation accuracy of 92.4% and an overall skill classification accuracy of 85.5% in replicating expert evaluations. These findings demonstrate the potential of the proposed method to provide objective, real-time feedback in microsurgical education, thereby enabling more standardized, data-driven training protocols and advancing competency assessment in high-stakes surgical environments.
Abstract (translated)
在神经外科中，显微吻合术的熟练掌握是一项关键的手术技能。该技术需要精确操作精细仪器的能力以确保成功的治疗结果。这些程序要求持续集中注意力、协调的手部动作以及高度精炼的运动技巧，这凸显了客观和系统方法评估及提升显微手术培训的需求。 传统评估方法通常依赖于专家监督手术过程或审查手术视频，这是一个主观性很强的过程，容易导致评分者之间的一致性问题、不稳定性，并需要大量的时间投入。这些局限性突出了自动化的、可扩展的解决方案的必要性。 为了解决这一挑战，我们引入了一种新颖的人工智能驱动框架，用于显微吻合术程序中的自动化动作分割和性能评估，旨在有效运行于边缘计算平台上。该提议系统由三个主要组成部分构成：（1）基于YOLO和DeepSORT的对象尖端跟踪与定位模块；（2）利用自相似矩阵进行动作边界检测和无监督聚类的动作分割模块；以及（3）用于评估手术手势熟练程度的监督分类模块。 通过使用包含58个专家评分显微吻合术视频的数据集进行了实验验证，证明了我们方法的有效性。在该数据集中，我们的系统达到了92.4%的帧级动作分割准确率和85.5%的整体技能分类准确率，在再现专家评估方面取得了显著成果。 这些发现表明所提出的方法具有提供显微外科教育中客观、实时反馈的潜力，从而能够促进更为标准化、基于数据驱动的培训协议，并在高风险手术环境中推进能力评估。
URL
https://arxiv.org/abs/2512.23942
