The assessment of surgical technical skills to be acquired by novice surgeons has been traditionally done by an expert surgeon and is therefore of a subjective nature. Nevertheless, the recent advances on IoT (Internet of Things), the possibility of incorporating sensors into objects and environments in order to collect large amounts of data, and the progress on machine learning are facilitating a more objective and automated assessment of surgical technical skills. This paper presents a systematic literature review of papers published after 2013 discussing the objective and automated assessment of surgical technical skills. 101 out of an initial list of 537 papers were analyzed to identify: 1) the sensors used; 2) the data collected by these sensors and the relationship between these data, surgical technical skills and surgeons’ levels of expertise; 3) the statistical methods and algorithms used to process these data; and 4) the feedback provided based on the outputs of these statistical methods and algorithms. Particularly, 1) mechanical and electromagnetic sensors are widely used for tool tracking, while inertial measurement units are widely used for body tracking; 2) path length, number of sub-movements, smoothness, fixation, saccade and total time are the main indicators obtained from raw data and serve to assess surgical technical skills such as economy, efficiency, hand tremor, or mind control, and distinguish between two or three levels of expertise (novice/intermediate/advanced surgeons); 3) SVM (Support Vector Machines) and Neural Networks are the preferred statistical methods and algorithms for processing the data collected, while new opportunities are opened up to combine various algorithms and use deep learning; and 4) feedback is provided by matching performance indicators and a lexicon of words and visualizations, although there is considerable room for research in the context of feedback and visualizations, taking, for example, ideas from learning analytics.

对新手外科医生将获得的外科技术技能的评估传统上由专家外科医生完成，因此具有主观性。尽管如此，IoT（物联网）的最新进展、将传感器集成到物体和环境中以收集大量数据的可能性以及机器学习的进步正在促进对外科技术技能进行更客观和自动化的评估。本文对 2013 年之后发表的论文进行了系统的文献综述，这些论文讨论了外科技术技能的客观和自动化评估。对最初的 537 篇论文中的 101 篇进行了分析，以确定：1) 使用的传感器；2）这些传感器收集的数据以及这些数据之间的关系，外科技术技能和外科医生的专业水平；3) 用于处理这些数据的统计方法和算法；4) 基于这些统计方法和算法的输出提供的反馈。特别是，1）机械和电磁传感器广泛用于工具跟踪，而惯性测量单元广泛用于人体跟踪；2) 路径长度、子动作次数、平滑度、注视、扫视和总时间是从原始数据中获得的主要指标，用于评估手术技术技能，如经济性、效率、手颤或精神控制，并区分两到三个级别的专业知识（新手/中级/高级外科医生）；3）SVM（支持向量机）和神经网络是处理收集到的数据的首选统计方法和算法，同时也开辟了结合各种算法和使用深度学习的新机会；4) 反馈是通过匹配的绩效指标和词汇和可视化提供的，尽管在反馈和可视化的背景下有相当大的研究空间，例如，学习分析的想法。