用图像分析客观评估显微血管吻合术，减少人为误差。
Quantitative Outcome-Oriented Assessment of Microsurgical Anastomosis
- 通过几何建模与图像处理实现误差自动检测与评分
- 在三组临床数据上验证，效果接近专家评分
- 适合显微外科培训与能力评估体系升级
显微血管吻合术对操作者的精细动作与空间感知能力要求极高，因此训练与能力评估至关重要。现有方法如基于结果的吻合失误指数常依赖主观判断，易引入偏差，影响评估的可靠性和效率。本研究利用三家医院不同水平参与者的数据集，提出一种基于图像处理的定量评估框架。该方法结合误差的几何建模与自动检测评分机制，显著提升了评估的客观性与效率，并推动了训练流程的优化。实验结果表明，所提出的几何指标能有效复现专家评分结果，对本文关注的各类误差具有良好的一致性。
原文摘要 · Abstract (English)
Microsurgical anastomosis demands exceptional dexterity and visuospatial skills, underscoring the importance of comprehensive training and precise outcome assessment. Currently, methods such as the outcome-oriented anastomosis lapse index are used to evaluate this procedure. However, they often rely on subjective judgment, which can introduce biases that affect the reliability and efficiency of the assessment of competence. Leveraging three datasets from hospitals with participants at various levels, we introduce a quantitative framework that uses image-processing techniques for objective assessment of microsurgical anastomoses. The approach uses geometric modeling of errors along with a detection and scoring mechanism, enhancing the efficiency and reliability of microsurgical proficiency assessment and advancing training protocols. The results show that the geometric metrics effectively replicate expert raters' scoring for the errors considered in this work.
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